diff --git a/.gitattributes b/.gitattributes
index 029f5ac2ec15ee52c180a3e1476d8d7aa8a09dbb..1456f141e5e74ff6e95af766f4b5600bada3bc69 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -37,3 +37,4 @@ qa/eval_nbest_predictions.json filter=lfs diff=lfs merge=lfs -text
 qa/sequential-policy-gradient.pdf filter=lfs diff=lfs merge=lfs -text
 sequential-policy-gradient.png filter=lfs diff=lfs merge=lfs -text
 examples/question-answering/qa/eval_nbest_predictions.json filter=lfs diff=lfs merge=lfs -text
+hpo-examples/question-answering/qa/eval_nbest_predictions.json filter=lfs diff=lfs merge=lfs -text
diff --git a/hpo-examples/audio-classification/ac/README.md b/hpo-examples/audio-classification/ac/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b29c0d3e4728d0c5cbf38479a829174e1c2eab86
--- /dev/null
+++ b/hpo-examples/audio-classification/ac/README.md
@@ -0,0 +1,88 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: facebook/wav2vec2-base
+tags:
+- audio-classification
+- generated_from_trainer
+datasets:
+- superb
+metrics:
+- accuracy
+model-index:
+- name: wav2vec2-base-ft-keyword-spotting
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: superb
+      type: superb
+      config: ks
+      split: validation
+      args: ks
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9826419535157399
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# wav2vec2-base-ft-keyword-spotting
+
+This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0954
+- Accuracy: 0.9826
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 48
+- eval_batch_size: 32
+- seed: 0
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 192
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 8.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 1.3624        | 1.0    | 267  | 1.1959          | 0.6546   |
+| 0.3854        | 2.0    | 534  | 0.2675          | 0.9734   |
+| 0.2473        | 3.0    | 801  | 0.1461          | 0.9768   |
+| 0.1997        | 4.0    | 1068 | 0.1088          | 0.9804   |
+| 0.1723        | 5.0    | 1335 | 0.0954          | 0.9826   |
+| 0.1442        | 6.0    | 1602 | 0.0927          | 0.9813   |
+| 0.1397        | 7.0    | 1869 | 0.0892          | 0.9812   |
+| 0.1368        | 7.9728 | 2128 | 0.0896          | 0.9812   |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/audio-classification/ac/all_results.json b/hpo-examples/audio-classification/ac/all_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..7c255135af726b082fa78566ad17225061d58c10
--- /dev/null
+++ b/hpo-examples/audio-classification/ac/all_results.json
@@ -0,0 +1,13 @@
+{
+    "epoch": 7.972769953051643,
+    "eval_accuracy": 0.9826419535157399,
+    "eval_loss": 0.09542840719223022,
+    "eval_runtime": 5.5538,
+    "eval_samples_per_second": 1224.023,
+    "eval_steps_per_second": 38.352,
+    "total_flos": 3.767900833756416e+18,
+    "train_loss": 0.5178930132572812,
+    "train_runtime": 756.2923,
+    "train_samples_per_second": 540.468,
+    "train_steps_per_second": 2.814
+}
\ No newline at end of file
diff --git a/hpo-examples/audio-classification/ac/config.json b/hpo-examples/audio-classification/ac/config.json
new file mode 100644
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--- /dev/null
+++ b/hpo-examples/audio-classification/ac/config.json
@@ -0,0 +1,147 @@
+{
+  "_name_or_path": "facebook/wav2vec2-base",
+  "activation_dropout": 0.0,
+  "adapter_attn_dim": null,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": false,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "finetuning_task": "audio-classification",
+  "freeze_feat_extract_train": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "yes",
+    "1": "no",
+    "10": "_silence_",
+    "11": "_unknown_",
+    "2": "up",
+    "3": "down",
+    "4": "left",
+    "5": "right",
+    "6": "on",
+    "7": "off",
+    "8": "stop",
+    "9": "go"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "_silence_": "10",
+    "_unknown_": "11",
+    "down": "3",
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+    "no": "1",
+    "off": "7",
+    "on": "6",
+    "right": "5",
+    "stop": "8",
+    "up": "2",
+    "yes": "0"
+  },
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "no_mask_channel_overlap": false,
+  "no_mask_time_overlap": false,
+  "num_adapter_layers": 3,
+  "num_attention_heads": 12,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "num_negatives": 100,
+  "output_hidden_size": 768,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 256,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32,
+  "xvector_output_dim": 512
+}
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+  "padding_value": 0.0,
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+  "sampling_rate": 16000
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+      "loss": 0.131,
+      "step": 2100
+    },
+    {
+      "epoch": 7.905164319248827,
+      "grad_norm": 1.2533661127090454,
+      "learning_rate": 2.9765013054830287e-07,
+      "loss": 0.1245,
+      "step": 2110
+    },
+    {
+      "epoch": 7.942723004694836,
+      "grad_norm": 1.472484827041626,
+      "learning_rate": 1.409921671018277e-07,
+      "loss": 0.1368,
+      "step": 2120
+    },
+    {
+      "epoch": 7.972769953051643,
+      "eval_accuracy": 0.9811709326272433,
+      "eval_loss": 0.08957477658987045,
+      "eval_runtime": 5.418,
+      "eval_samples_per_second": 1254.697,
+      "eval_steps_per_second": 39.313,
+      "step": 2128
+    },
+    {
+      "epoch": 7.972769953051643,
+      "step": 2128,
+      "total_flos": 3.767900833756416e+18,
+      "train_loss": 0.5178930132572812,
+      "train_runtime": 756.2923,
+      "train_samples_per_second": 540.468,
+      "train_steps_per_second": 2.814
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 2128,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 8,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 3.767900833756416e+18,
+  "train_batch_size": 48,
+  "trial_name": null,
+  "trial_params": null
+}
diff --git a/hpo-examples/audio-classification/ac/training_args.bin b/hpo-examples/audio-classification/ac/training_args.bin
new file mode 100644
index 0000000000000000000000000000000000000000..8cb93f462e7a7099dc99685736eebe15275e5761
--- /dev/null
+++ b/hpo-examples/audio-classification/ac/training_args.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11e71a8faf8833e9bfb138217263fb0518314e3b8597902b752b2bc9dd143942
+size 5368
diff --git a/hpo-examples/audio-classification/requirements.txt b/hpo-examples/audio-classification/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..acf058d4cf46eaa3e7fe55012a9ecd9982a12bf3
--- /dev/null
+++ b/hpo-examples/audio-classification/requirements.txt
@@ -0,0 +1,5 @@
+datasets>=1.14.0
+evaluate
+librosa
+torchaudio
+torch>=1.6
\ No newline at end of file
diff --git a/hpo-examples/audio-classification/run.sh b/hpo-examples/audio-classification/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..11c28101f928dea59c215d76b349a5436bd2782d
--- /dev/null
+++ b/hpo-examples/audio-classification/run.sh
@@ -0,0 +1,30 @@
+CUDA_VISIBLE_DEVICES=0 python run_audio_classification.py \
+    --model_name_or_path facebook/wav2vec2-base \
+    --dataset_name superb \
+    --dataset_config_name ks \
+    --trust_remote_code \
+    --output_dir wav2vec2-base-ft-keyword-spotting \
+    --overwrite_output_dir \
+    --remove_unused_columns False \
+    --do_train \
+    --do_eval \
+    --fp16 \
+    --learning_rate 3e-5 \
+    --max_length_seconds 1 \
+    --attention_mask False \
+    --warmup_ratio 0.1 \
+    --num_train_epochs 8 \
+    --per_device_train_batch_size 64 \
+    --gradient_accumulation_steps 4 \
+    --per_device_eval_batch_size 32 \
+    --dataloader_num_workers 4 \
+    --logging_strategy steps \
+    --logging_steps 10 \
+    --eval_strategy epoch \
+    --save_strategy epoch \
+    --load_best_model_at_end True \
+    --metric_for_best_model accuracy \
+    --save_total_limit 3 \
+    --seed 0 \
+    --push_to_hub \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
\ No newline at end of file
diff --git a/hpo-examples/audio-classification/run_audio_classification.py b/hpo-examples/audio-classification/run_audio_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..04c8e0b958403cd677cb177d60234f74b9ba86f0
--- /dev/null
+++ b/hpo-examples/audio-classification/run_audio_classification.py
@@ -0,0 +1,462 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import sys
+import warnings
+from dataclasses import dataclass, field
+from random import randint
+from typing import Optional, List
+
+import datasets
+import evaluate
+import numpy as np
+from datasets import DatasetDict, load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForAudioClassification,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+from trplib import apply_trp
+
+
+logger = logging.getLogger(__name__)
+
+# # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+# check_min_version("4.50.0.dev0")
+
+require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
+
+
+def random_subsample(wav: np.ndarray, max_length: float, sample_rate: int = 16000):
+    """Randomly sample chunks of `max_length` seconds from the input audio"""
+    sample_length = int(round(sample_rate * max_length))
+    if len(wav) <= sample_length:
+        return wav
+    random_offset = randint(0, len(wav) - sample_length - 1)
+    return wav[random_offset : random_offset + sample_length]
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: Optional[str] = field(default=None, metadata={"help": "Name of a dataset from the datasets package"})
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "A file containing the training audio paths and labels."}
+    )
+    eval_file: Optional[str] = field(
+        default=None, metadata={"help": "A file containing the validation audio paths and labels."}
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="validation",
+        metadata={
+            "help": (
+                "The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    label_column_name: str = field(
+        default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"}
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_length_seconds: float = field(
+        default=20,
+        metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."},
+    )
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        default="facebook/wav2vec2-base",
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None, metadata={"help": "Name or path of preprocessor config."}
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    attention_mask: bool = field(
+        default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."}
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    freeze_feature_extractor: Optional[bool] = field(
+        default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
+    )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
+
+    apply_trp: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to apply trp or not."},
+    )
+    trp_depths: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "TRP depth value."
+        },
+    )
+    trp_p: Optional[float] = field(
+        default=0.1,
+        metadata={
+            "help": "TRP p value."
+        },
+    )
+    trp_lambdas: Optional[List[float]] = field(
+        default_factory=lambda: [0.4, 0.2, 0.1],
+        metadata={
+            "help": "TRP lambda values (list of floats)."
+        },
+    )
+
+    def __post_init__(self):
+        if not self.freeze_feature_extractor and self.freeze_feature_encoder:
+            warnings.warn(
+                "The argument `--freeze_feature_extractor` is deprecated and "
+                "will be removed in a future version. Use `--freeze_feature_encoder` "
+                "instead. Setting `freeze_feature_encoder==True`.",
+                FutureWarning,
+            )
+        if self.freeze_feature_extractor and not self.freeze_feature_encoder:
+            raise ValueError(
+                "The argument `--freeze_feature_extractor` is deprecated and "
+                "should not be used in combination with `--freeze_feature_encoder`. "
+                "Only make use of `--freeze_feature_encoder`."
+            )
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_audio_classification", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to train from scratch."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Initialize our dataset and prepare it for the audio classification task.
+    raw_datasets = DatasetDict()
+    raw_datasets["train"] = load_dataset(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        split=data_args.train_split_name,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    raw_datasets["eval"] = load_dataset(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        split=data_args.eval_split_name,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+
+    if data_args.audio_column_name not in raw_datasets["train"].column_names:
+        raise ValueError(
+            f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(raw_datasets['train'].column_names)}."
+        )
+
+    if data_args.label_column_name not in raw_datasets["train"].column_names:
+        raise ValueError(
+            f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--label_column_name` to the correct text column - one of "
+            f"{', '.join(raw_datasets['train'].column_names)}."
+        )
+
+    # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
+    # transformer outputs in the classifier, but it doesn't always lead to better accuracy
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name or model_args.model_name_or_path,
+        return_attention_mask=model_args.attention_mask,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+
+    # `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+    )
+
+    model_input_name = feature_extractor.model_input_names[0]
+
+    def train_transforms(batch):
+        """Apply train_transforms across a batch."""
+        subsampled_wavs = []
+        for audio in batch[data_args.audio_column_name]:
+            wav = random_subsample(
+                audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate
+            )
+            subsampled_wavs.append(wav)
+        inputs = feature_extractor(subsampled_wavs, sampling_rate=feature_extractor.sampling_rate)
+        output_batch = {model_input_name: inputs.get(model_input_name)}
+        output_batch["labels"] = list(batch[data_args.label_column_name])
+
+        return output_batch
+
+    def val_transforms(batch):
+        """Apply val_transforms across a batch."""
+        wavs = [audio["array"] for audio in batch[data_args.audio_column_name]]
+        inputs = feature_extractor(wavs, sampling_rate=feature_extractor.sampling_rate)
+        output_batch = {model_input_name: inputs.get(model_input_name)}
+        output_batch["labels"] = list(batch[data_args.label_column_name])
+
+        return output_batch
+
+    # Prepare label mappings.
+    # We'll include these in the model's config to get human readable labels in the Inference API.
+    labels = raw_datasets["train"].features[data_args.label_column_name].names
+    label2id, id2label = {}, {}
+    for i, label in enumerate(labels):
+        label2id[label] = str(i)
+        id2label[str(i)] = label
+
+    # Load the accuracy metric from the datasets package
+    metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
+
+    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
+    # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
+    def compute_metrics(eval_pred):
+        """Computes accuracy on a batch of predictions"""
+        predictions = np.argmax(eval_pred.predictions, axis=1)
+        return metric.compute(predictions=predictions, references=eval_pred.label_ids)
+
+    config = AutoConfig.from_pretrained(
+        model_args.config_name or model_args.model_name_or_path,
+        num_labels=len(labels),
+        label2id=label2id,
+        id2label=id2label,
+        finetuning_task="audio-classification",
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    model = AutoModelForAudioClassification.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
+    )
+
+    # freeze the convolutional waveform encoder
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.apply_trp:
+        model = apply_trp(model, depths=model_args.trp_depths, p=model_args.trp_p, lambdas=model_args.trp_lambdas)
+
+    if training_args.do_train:
+        if data_args.max_train_samples is not None:
+            raw_datasets["train"] = (
+                raw_datasets["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
+            )
+        # Set the training transforms
+        raw_datasets["train"].set_transform(train_transforms, output_all_columns=False)
+
+    if training_args.do_eval:
+        if data_args.max_eval_samples is not None:
+            raw_datasets["eval"] = (
+                raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
+            )
+        # Set the validation transforms
+        raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False)
+
+    # Initialize our trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=raw_datasets["train"] if training_args.do_train else None,
+        eval_dataset=raw_datasets["eval"] if training_args.do_eval else None,
+        compute_metrics=compute_metrics,
+        processing_class=feature_extractor,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate()
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Write model card and (optionally) push to hub
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "audio-classification",
+        "dataset": data_args.dataset_name,
+        "tags": ["audio-classification"],
+    }
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hpo-examples/audio-classification/trplib.py b/hpo-examples/audio-classification/trplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0fb33879b1ab05f53e0b8e7068729abbb96146
--- /dev/null
+++ b/hpo-examples/audio-classification/trplib.py
@@ -0,0 +1,1181 @@
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from torchvision.models.mobilenetv2 import MobileNetV2
+from torchvision.models.resnet import ResNet
+from torchvision.models.efficientnet import EfficientNet
+from torchvision.models.vision_transformer import VisionTransformer
+from torchvision.models.segmentation.fcn import FCN
+from torchvision.models.segmentation.deeplabv3 import DeepLabV3
+
+import transformers
+from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, CausalLMOutput, Seq2SeqLMOutput
+
+from typing import Optional, Tuple, List, Union, Callable
+from collections import OrderedDict
+import types
+
+
+def trp_criterion(trp_blocks: nn.ModuleList, shared_head: Callable, criterion: Callable, lambdas: List[float], hidden_states: Tensor, logits: Tensor, targets: Tensor, loss_normalization=False):
+    loss, mask = criterion(logits, targets)
+    if loss_normalization:
+        coeff = loss.detach()
+
+    embeds = [hidden_states]
+    predictions = []
+    for k, c in enumerate(lambdas):
+        embeds.append(trp_blocks[k](embeds[-1]))
+        predictions.append(shared_head(embeds[-1]))
+        replica_loss, mask = criterion(predictions[-1], targets, mask)
+        loss += c * replica_loss
+    
+    if loss_normalization:
+        with torch.no_grad():
+            coeff = torch.exp(coeff) / torch.exp(loss.detach())
+        loss = coeff * loss
+    
+    return loss
+
+
+class TPBlock(nn.Module):
+    def __init__(self, depths: int, in_features: int, p: float, dim=-1):
+        super(TPBlock, self).__init__()
+
+        self.dropout = nn.Dropout(p)
+
+        self.cdim = dim
+
+        blocks = []
+        for _ in range(depths):
+            blocks.append(nn.Linear(in_features, in_features))
+            nn.init.constant_(blocks[-1].weight, 0.0)
+            nn.init.constant_(blocks[-1].bias, 0.0)
+            blocks.append(nn.ReLU())
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        x = self.dropout(x)
+        if self.cdim == -1:
+            x = x + self.blocks(x)
+        else:
+            x = x + torch.movedim(self.blocks(torch.movedim(x, self.cdim, -1)), -1, self.cdim)
+        return x
+    
+
+class Config:
+    @staticmethod
+    def gen_criterion(*args, **kwargs):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor.
+                target (Tensor): Target labels.
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor with the same shape of target.
+            """
+            pass
+        return func
+    
+    @staticmethod
+    def gen_shared_head(*args, **kwargs):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States tensor.
+
+            Returns:
+                logits (Tensor): Logits tensor.
+            """
+            pass
+        return func
+
+    @staticmethod
+    def forward(*args, **kwargs):
+        pass
+
+
+# Wav2Vec2 for Audio Classification
+class Wav2Vec2ForSequenceClassificationConfig(Config): 
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self, attention_mask):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+            output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            if self.config.use_weighted_layer_sum:
+                hidden_states = outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION]
+                hidden_states = torch.stack(hidden_states, dim=1)
+                norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+                hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+            else:
+                hidden_states = outputs[0]
+
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                shared_head = Wav2Vec2ForSequenceClassificationConfig.gen_shared_head(self, attention_mask)
+                criterion = Wav2Vec2ForSequenceClassificationConfig.gen_criterion()
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states, logits.view(-1, self.config.num_labels),  labels.view(-1), loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# MobileNetV2 for Image Classification
+class MobileNetV2Config(Config):
+    @staticmethod
+    def gen_criterion(label_smoothing=0.0, top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B] or [B, C].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            label = torch.argmax(target, dim=1) if label_smoothing > 0.0 else target
+                
+            unmasked_loss = F.cross_entropy(input, label, reduction="none", label_smoothing=label_smoothing)
+            if mask is None:
+                mask = torch.ones_like(unmasked_loss, dtype=torch.float32, device=target.device)
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=-1)
+                mask = mask * torch.eq(topk_indices, label[:, None]).any(dim=-1).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = MobileNetV2Config.gen_shared_head(self)
+                criterion = MobileNetV2Config.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ResNet for Image Classification
+class ResNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.fc(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.conv1(images)
+            x = self.bn1(x)
+            x = self.relu(x)
+            x = self.maxpool(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.fc(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = ResNetConfig.gen_shared_head(self)
+                criterion = ResNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# EfficientNet for Image Classification
+class EfficientNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = EfficientNetConfig.gen_shared_head(self)
+                criterion = EfficientNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ViT for Image Classification
+class VisionTransformerConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.heads(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self._process_input(images)
+            n = x.shape[0]
+            batch_class_token = self.class_token.expand(n, -1, -1)
+            x = torch.cat([batch_class_token, x], dim=1)
+            x = self.encoder(x)
+            x = x[:, 0]
+
+            logits = self.heads(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = VisionTransformerConfig.gen_shared_head(self)
+                criterion = VisionTransformerConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# Bert for Question Answering
+class BertForQuestionAnsweringConfig(Config): 
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target: List[Tensor], mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C, 2].
+                target (List[Tensor]): 
+                    Start Positions of shape [B].
+                    End Positions of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            start_positions, end_positions = target
+
+            if mask is None:
+                mask = torch.ones_like(start_positions, dtype=torch.float32, device=start_positions.device)
+
+            start_logits, end_logits = input.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            masked_start_losses = F.cross_entropy(start_logits, start_positions, ignore_index=ignored_index, reduction="none")
+            start_loss = torch.sum(mask * masked_start_losses) / (torch.sum(mask) + 1e-6)
+            masked_end_losses = F.cross_entropy(end_logits, end_positions, ignore_index=ignored_index, reduction="none")
+            end_loss = torch.sum(mask * masked_end_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(start_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, start_positions[:, None]).any(dim=1).to(start_logits.dtype)
+                topk_values, topk_indices = torch.topk(end_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, end_positions[:, None]).any(dim=1).to(end_logits.dtype)
+
+            return (start_loss + end_loss) / 2, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.qa_outputs(hidden_states)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            start_positions: Optional[torch.Tensor] = None,
+            end_positions: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+            r"""
+            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the start of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the end of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            sequence_output = outputs[0]
+
+            logits = self.qa_outputs(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                shared_head = BertForQuestionAnsweringConfig.gen_shared_head(self)
+                criterion = BertForQuestionAnsweringConfig.gen_criterion()
+                total_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, [start_positions, end_positions], loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+    
+
+# FCN for Semantic Segmentation
+class FCNConfig(Config):
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input Tensor: input tensor of shape [B, C, H, W].
+                target (Tensor): Target labels of shape [B, H, W].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B, H, W].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+                
+            masked_loss = F.cross_entropy(input, target, ignore_index=255, reduction="none")
+            loss = torch.sum(mask * masked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, target[:, None, :, :]).any(dim=1).to(input.dtype)
+                # mask = mask * torch.eq(torch.argmax(x, dim=1), target).to(x.dtype)
+                
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_out_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+    
+    @staticmethod
+    def gen_aux_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.aux_classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            input_shape = images.shape[-2:]
+            # contract: features is a dict of tensors
+            features = self.backbone(images)
+
+            result = OrderedDict()
+            x = features["out"]
+            x = self.classifier(x)
+            x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            result["out"] = x
+
+            if self.aux_classifier is not None:
+                x = features["aux"]
+                x = self.aux_classifier(x)
+                x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+                result["aux"] = x
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                out_shared_head = FCNConfig.gen_out_shared_head(self, input_shape)
+                aux_shared_head = FCNConfig.gen_aux_shared_head(self, input_shape)
+                criterion = FCNConfig.gen_criterion(top_k)
+                out_loss = trp_criterion(self.out_trp_blocks, out_shared_head, criterion, lambdas, features["out"], result["out"], targets, loss_normalization)
+                aux_loss = trp_criterion(self.aux_trp_blocks, aux_shared_head, criterion, lambdas, features["aux"], result["aux"], targets, loss_normalization)
+                loss = out_loss + 0.5 * aux_loss
+                return result, loss
+            return result
+        return func
+
+
+# DeepLabV3Config for Semantic Segmentation
+class DeepLabV3Config(FCNConfig):
+    pass
+
+
+# Bert for Text Classification
+class BertForSequenceClassificationConfig(Config): 
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, pooled_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Boberta for Text Classification
+class RobertaForSequenceClassificationConfig(BertForSequenceClassificationConfig): 
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.roberta(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            sequence_output = outputs[0]
+            logits = self.classifier(sequence_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                # move labels to correct device to enable model parallelism
+                labels = labels.to(logits.device)
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Wav2Vec2 for Speech Recognition
+class Wav2Vec2ForCTCConfig(Config):
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def greedy_decode_ctc(
+        log_probs: torch.Tensor,
+        input_lengths: torch.Tensor,
+        blank_token_id: int,
+        target_lengths: torch.Tensor
+    ):
+        """
+        Convert logits to flattened predictions that match the shape of flattened_targets.
+
+        Args:
+            log_probs: [B, L, V] - log-softmax output
+            input_lengths: [B] - actual length of each input
+            blank_token_id: int - index of blank token
+            target_lengths: [B] - used to determine how many predictions to keep per sample
+
+        Returns:
+            flattened_predictions: 1D tensor, same total length as sum(target_lengths)
+        """
+        batch_size = log_probs.size(0)
+        decoded_all = []
+
+        predicted_ids = log_probs.argmax(dim=-1)  # [B, L]
+
+        for i in range(batch_size):
+            pred = predicted_ids[i][:input_lengths[i]]  # [Li]
+            prev = None
+            decoded = []
+            for token in pred:
+                token = token.item()
+                if token != blank_token_id and token != prev:
+                    decoded.append(token)
+                prev = token
+            # Trim or pad to match target_lengths[i]
+            tgt_len = target_lengths[i].item()
+            if len(decoded) >= tgt_len:
+                decoded = decoded[:tgt_len]
+            else:
+                decoded = decoded + [blank_token_id] * (tgt_len - len(decoded))  # pad with blank
+            decoded_all.extend(decoded)
+
+        return torch.tensor(decoded_all, dtype=torch.long, device=log_probs.device)  # shape: [sum(target_lengths)]
+
+    @staticmethod
+    def gen_criterion(input_lengths: Tensor, pad_token_id: int, ctc_zero_infinity: bool):
+        def func(logits: Tensor, labels: Tensor, mask=None):
+            """
+            Args:
+                logits (Tensor): Log Probablities of shape [B, L, V].
+                labels (Tensor): Flattened Targets of shape [B, L'].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(input_lengths, dtype=torch.float32, device=input_lengths.device)
+
+            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            with torch.backends.cudnn.flags(enabled=False):
+                masked_losses = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=pad_token_id, reduction="none", zero_infinity=ctc_zero_infinity)
+                loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                thres = 0.5
+                flattened_predictions = Wav2Vec2ForCTCConfig.greedy_decode_ctc(
+                    log_probs.transpose(0, 1),  # [B, T, V]
+                    input_lengths=input_lengths,
+                    blank_token_id=pad_token_id,
+                    target_lengths=target_lengths
+                )
+                token_wise_mask =  torch.eq(flattened_predictions, flattened_targets).to(flattened_targets.dtype)
+                segment_ids = torch.arange(len(target_lengths), device=target_lengths.device).repeat_interleave(target_lengths)
+                sequence_wise_mask = torch.zeros(len(target_lengths), dtype=target_lengths.dtype, device=token_wise_mask.device).scatter_add(0, segment_ids, token_wise_mask)
+                mask = mask * torch.ge(sequence_wise_mask, thres * target_lengths).to(flattened_targets.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.lm_head(hidden_states)
+            # log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, CausalLMOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
+                Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
+                the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
+                All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
+                config.vocab_size - 1]`.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None and labels.max() >= self.config.vocab_size:
+                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            hidden_states = self.dropout(hidden_states)
+
+            logits = self.lm_head(hidden_states)
+
+            loss = None
+            if labels is not None:
+                # retrieve loss input_lengths from attention_mask
+                attention_mask = (
+                    attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
+                )
+                input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+                shared_head = Wav2Vec2ForCTCConfig.gen_shared_head(self)
+                criterion = Wav2Vec2ForCTCConfig.gen_criterion(input_lengths, self.config.pad_token_id, self.config.ctc_zero_infinity)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states,  logits, labels, loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForCTCConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return CausalLMOutput(
+                loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+            )
+        return func
+
+
+# MBart for Translation
+class MBartForConditionalGenerationConfig(Config): 
+    @staticmethod
+    def gen_criterion(vocab_size: int, top_k=1):
+        def func(logits, labels, mask=None):
+            """
+            Args:
+                logits (Tensor): Logits tensor of shape [B, L, V].
+                labels (Tensor): Target labels of shape [B, L].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(labels.view(-1), dtype=torch.float32, device=labels.device)
+                
+            masked_losses = F.cross_entropy(logits.view(-1, vocab_size), labels.view(-1), reduction="none")
+            loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(logits.view(-1, vocab_size), top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, labels.view(-1, 1)).any(dim=1).to(logits.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, L].
+            """
+            logits = self.lm_head(hidden_states) + self.final_logits_bias
+            return logits
+        return func
+    
+    @staticmethod
+    def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+        """
+        Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
+        have a single `decoder_start_token_id` in contrast to other Bart-like models.
+        """
+        prev_output_tokens = input_ids.clone()
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+        index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+        decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+        prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+        prev_output_tokens[:, 0] = decoder_start_tokens
+
+        return prev_output_tokens
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            Returns:
+
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None:
+                # if use_cache:
+                #     logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+                use_cache = False
+                if decoder_input_ids is None and decoder_inputs_embeds is None:
+                    decoder_input_ids = MBartForConditionalGenerationConfig.shift_tokens_right(labels, self.config.pad_token_id)
+
+            outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=decoder_input_ids,
+                encoder_outputs=encoder_outputs,
+                decoder_attention_mask=decoder_attention_mask,
+                head_mask=head_mask,
+                decoder_head_mask=decoder_head_mask,
+                cross_attn_head_mask=cross_attn_head_mask,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+            masked_lm_loss = None
+            if labels is not None:
+                shared_head = MBartForConditionalGenerationConfig.gen_shared_head(self)
+                criterion = MBartForConditionalGenerationConfig.gen_criterion(self.config.vocab_size)
+                masked_lm_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, outputs[0],  lm_logits, labels, loss_normalization)
+
+            if not return_dict:
+                output = (lm_logits,) + outputs[1:]
+                return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+            return Seq2SeqLMOutput(
+                loss=masked_lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                decoder_hidden_states=outputs.decoder_hidden_states,
+                decoder_attentions=outputs.decoder_attentions,
+                cross_attentions=outputs.cross_attentions,
+                encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+                encoder_hidden_states=outputs.encoder_hidden_states,
+                encoder_attentions=outputs.encoder_attentions,
+            )
+        return func
+
+
+def apply_trp(model, depths: int, p: float, lambdas: List[float], **kwargs):
+    if isinstance(model, transformers.Wav2Vec2ForSequenceClassification):
+        print("✅ Applying TRP to Wav2Vec2 for Audio Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, MobileNetV2):
+        print("✅ Applying TRP to MobileNetV2 for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(MobileNetV2Config.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, ResNet):
+        print("✅ Applying TRP to ResNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p) for _ in lambdas])
+        model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, EfficientNet):
+        print("✅ Applying TRP to EfficientNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(EfficientNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, VisionTransformer):
+        print("✅ Applying TRP to VisionTransformer for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(VisionTransformerConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, transformers.BertForQuestionAnswering):
+        print("✅ Applying TRP to Bert for Question Answering...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForQuestionAnsweringConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, FCN):
+        print("✅ Applying TRP to FCN for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(FCNConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, DeepLabV3):
+        print("✅ Applying TRP to DeepLabV3 for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(DeepLabV3Config.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, transformers.BertForSequenceClassification):
+        print("✅ Applying TRP to Bert for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.RobertaForSequenceClassification):
+        print("✅ Applying TRP to Roberta for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(RobertaForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.Wav2Vec2ForCTC):
+        print("✅ Applying TRP to Wav2Vec2 for Speech Recognition...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForCTCConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.MBartForConditionalGeneration):
+        print("✅ Applying TRP to MBart for Translation...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(MBartForConditionalGenerationConfig.gen_forward(lambdas, False), model)
+    else:
+        torch._assert(
+            isinstance(model, transformers.Wav2Vec2ForSequenceClassification), 
+            "The model should be an object of [`Wav2Vec2ForSequenceClassification`].")
+
+    return model
\ No newline at end of file
diff --git a/hpo-examples/image-classification/__pycache__/presets.cpython-310.pyc b/hpo-examples/image-classification/__pycache__/presets.cpython-310.pyc
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diff --git a/hpo-examples/image-classification/__pycache__/sampler.cpython-310.pyc b/hpo-examples/image-classification/__pycache__/sampler.cpython-310.pyc
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index 0000000000000000000000000000000000000000..3d6497dfb5997d130545c54b08fc389e45988505
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diff --git a/hpo-examples/image-classification/__pycache__/transforms.cpython-310.pyc b/hpo-examples/image-classification/__pycache__/transforms.cpython-310.pyc
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diff --git a/hpo-examples/image-classification/efficientnet_v2_m/model_7.pth b/hpo-examples/image-classification/efficientnet_v2_m/model_7.pth
new file mode 100644
index 0000000000000000000000000000000000000000..9e8fd32ccc767a620fe395977575d1be28906377
--- /dev/null
+++ b/hpo-examples/image-classification/efficientnet_v2_m/model_7.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f4b45a082517a3e60498e92710f5a97b5869515db52e536d9c92a3b68ae4e8f
+size 454515355
diff --git a/hpo-examples/image-classification/mobilenetv2/model_32.pth b/hpo-examples/image-classification/mobilenetv2/model_32.pth
new file mode 100644
index 0000000000000000000000000000000000000000..61c5fe44e07cc57e15e88816fdce84fd3d203050
--- /dev/null
+++ b/hpo-examples/image-classification/mobilenetv2/model_32.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f240e3b954e1b1786878733c3045125461fa67bcb6ce50a200d5ec6e46081bc7
+size 48002008
diff --git a/hpo-examples/image-classification/presets.py b/hpo-examples/image-classification/presets.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d1bf1cc71455fcb043538fecbad54050671d015
--- /dev/null
+++ b/hpo-examples/image-classification/presets.py
@@ -0,0 +1,71 @@
+import torch
+from torchvision.transforms import autoaugment, transforms
+from torchvision.transforms.functional import InterpolationMode
+
+
+class ClassificationPresetTrain:
+    def __init__(
+        self,
+        *,
+        crop_size,
+        mean=(0.485, 0.456, 0.406),
+        std=(0.229, 0.224, 0.225),
+        interpolation=InterpolationMode.BILINEAR,
+        hflip_prob=0.5,
+        auto_augment_policy=None,
+        ra_magnitude=9,
+        augmix_severity=3,
+        random_erase_prob=0.0,
+    ):
+        trans = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)]
+        if hflip_prob > 0:
+            trans.append(transforms.RandomHorizontalFlip(hflip_prob))
+        if auto_augment_policy is not None:
+            if auto_augment_policy == "ra":
+                trans.append(autoaugment.RandAugment(interpolation=interpolation, magnitude=ra_magnitude))
+            elif auto_augment_policy == "ta_wide":
+                trans.append(autoaugment.TrivialAugmentWide(interpolation=interpolation))
+            elif auto_augment_policy == "augmix":
+                trans.append(autoaugment.AugMix(interpolation=interpolation, severity=augmix_severity))
+            else:
+                aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)
+                trans.append(autoaugment.AutoAugment(policy=aa_policy, interpolation=interpolation))
+        trans.extend(
+            [
+                transforms.PILToTensor(),
+                transforms.ConvertImageDtype(torch.float),
+                transforms.Normalize(mean=mean, std=std),
+            ]
+        )
+        if random_erase_prob > 0:
+            trans.append(transforms.RandomErasing(p=random_erase_prob))
+
+        self.transforms = transforms.Compose(trans)
+
+    def __call__(self, img):
+        return self.transforms(img)
+
+
+class ClassificationPresetEval:
+    def __init__(
+        self,
+        *,
+        crop_size,
+        resize_size=256,
+        mean=(0.485, 0.456, 0.406),
+        std=(0.229, 0.224, 0.225),
+        interpolation=InterpolationMode.BILINEAR,
+    ):
+
+        self.transforms = transforms.Compose(
+            [
+                transforms.Resize(resize_size, interpolation=interpolation),
+                transforms.CenterCrop(crop_size),
+                transforms.PILToTensor(),
+                transforms.ConvertImageDtype(torch.float),
+                transforms.Normalize(mean=mean, std=std),
+            ]
+        )
+
+    def __call__(self, img):
+        return self.transforms(img)
diff --git a/hpo-examples/image-classification/resnet50/model_35.pth b/hpo-examples/image-classification/resnet50/model_35.pth
new file mode 100644
index 0000000000000000000000000000000000000000..d5d98d08c47b71694981c4fd56bea430c59a4ef5
--- /dev/null
+++ b/hpo-examples/image-classification/resnet50/model_35.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b304e1309e3143b9e7109d5b7263369e4e93c949dde9bab44b3d3b193d16361
+size 255177167
diff --git a/hpo-examples/image-classification/run.sh b/hpo-examples/image-classification/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..19917746f185190abf73201b85eeba299206ae15
--- /dev/null
+++ b/hpo-examples/image-classification/run.sh
@@ -0,0 +1,49 @@
+# ✅ --lr 0.00002 Acc@1 71.878 Acc@5 90.286 ->  Acc@1 72.104 Acc@5 90.316  (with normalization)
+torchrun --nproc_per_node=4 train.py\
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model mobilenet_v2  --output-dir mobilenet_v2 --weights MobileNet_V2_Weights.IMAGENET1K_V1\
+    --batch-size 192 --epochs 40 --lr 0.0004 --lr-step-size 10 --lr-gamma 0.5 --wd 0.00004 --apply-trp --trp-depths 1 --trp-p 0.15 --trp-lambdas 0.4 0.2 0.1
+# torchrun --nproc_per_node=4 train.py\
+#     --data-path /home/cs/Documents/datasets/imagenet\
+#     --model mobilenet_v2  --resume mobilenet_v2/model_32.pth --test-only
+
+
+# ✅ --lr 0.0002 Acc@1 76.130 Acc@5 92.862 -> Acc@1 77.234 Acc@5 93.322  (with normalization)
+torchrun --nproc_per_node=4 train.py\
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model resnet50 --output-dir resnet50 --weights ResNet50_Weights.IMAGENET1K_V1\
+    --batch-size 64 --epochs 40 --lr 0.0004 --lr-step-size 10 --lr-gamma 0.5 --print-freq 100\
+    --apply-trp --trp-depths 1 --trp-p 0.2 --trp-lambdas 0.4 0.2 0.1
+# torchrun --nproc_per_node=4 train.py\
+#     --data-path /home/cs/Documents/datasets/imagenet\
+#     --model resnet50  --resume resnet50/model_35.pth --test-only
+
+
+# ✅ Test:  Acc@1 85.218 Acc@5 97.208
+torchrun --nproc_per_node=4 train.py \
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model efficientnet_v2_m --output-dir efficientnet_v2_m --weights EfficientNet_V2_M_Weights.IMAGENET1K_V1\
+    --epochs 10 --batch-size 64 --lr 5e-9 --lr-scheduler cosineannealinglr --weight-decay 0.00002 \
+    --lr-warmup-method constant --lr-warmup-epochs 8 --lr-warmup-decay 0. \
+    --auto-augment ta_wide --random-erase 0.1 --label-smoothing 0.1 --mixup-alpha 0.2 --cutmix-alpha 1.0 --norm-weight-decay 0.0 \
+    --train-crop-size 384 --val-crop-size 480 --val-resize-size 480 --ra-sampler --ra-reps 4 --print-freq 100\
+    --apply-trp --trp-depths 1 --trp-p 0.2 --trp-lambdas 0.4 0.2 0.1
+# torchrun --nproc_per_node=4 train.py\
+#     --data-path /home/cs/Documents/datasets/imagenet\
+#     --model efficientnet_v2_m  --resume efficientnet_v2_m/model_7.pth --test-only\
+#      --val-crop-size 480 --val-resize-size 480
+
+
+# ✅ Test:  Acc@1 81.092 Acc@5 95.304
+torchrun --nproc_per_node=4 train.py\
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model vit_b_16 --output-dir vit_b_16 --weights ViT_B_16_Weights.IMAGENET1K_V1\
+    --epochs 5 --batch-size 196 --opt adamw --lr 5e-9 --lr-scheduler cosineannealinglr --wd 0.3\
+    --lr-warmup-method constant --lr-warmup-epochs 3 --lr-warmup-decay 0. \
+    --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra --clip-grad-norm 1 --cutmix-alpha 1.0\
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1 --print-freq 100
+# torchrun --nproc_per_node=4 train.py\
+#     --data-path /home/cs/Documents/datasets/imagenet\
+#     --model vit_b_16  --resume vit_b_16/model_4.pth --test-only
+
+
diff --git a/hpo-examples/image-classification/sampler.py b/hpo-examples/image-classification/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9dc1735a585cd2b46c50d7cf9389767f081a4dd
--- /dev/null
+++ b/hpo-examples/image-classification/sampler.py
@@ -0,0 +1,62 @@
+import math
+
+import torch
+import torch.distributed as dist
+
+
+class RASampler(torch.utils.data.Sampler):
+    """Sampler that restricts data loading to a subset of the dataset for distributed,
+    with repeated augmentation.
+    It ensures that different each augmented version of a sample will be visible to a
+    different process (GPU).
+    Heavily based on 'torch.utils.data.DistributedSampler'.
+
+    This is borrowed from the DeiT Repo:
+    https://github.com/facebookresearch/deit/blob/main/samplers.py
+    """
+
+    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, seed=0, repetitions=3):
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available!")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available!")
+            rank = dist.get_rank()
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = int(math.ceil(len(self.dataset) * float(repetitions) / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.num_selected_samples = int(math.floor(len(self.dataset) // 256 * 256 / self.num_replicas))
+        self.shuffle = shuffle
+        self.seed = seed
+        self.repetitions = repetitions
+
+    def __iter__(self):
+        if self.shuffle:
+            # Deterministically shuffle based on epoch
+            g = torch.Generator()
+            g.manual_seed(self.seed + self.epoch)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = list(range(len(self.dataset)))
+
+        # Add extra samples to make it evenly divisible
+        indices = [ele for ele in indices for i in range(self.repetitions)]
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # Subsample
+        indices = indices[self.rank : self.total_size : self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices[: self.num_selected_samples])
+
+    def __len__(self):
+        return self.num_selected_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
diff --git a/hpo-examples/image-classification/train.py b/hpo-examples/image-classification/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3112b967deb38b9628477a0a4ada0048045ca32
--- /dev/null
+++ b/hpo-examples/image-classification/train.py
@@ -0,0 +1,524 @@
+import datetime
+import os
+import time
+import warnings
+
+import presets
+import torch
+import torch.utils.data
+import torchvision
+import transforms
+import utils
+from sampler import RASampler
+from torch import nn
+from torch.utils.data.dataloader import default_collate
+from torchvision.transforms.functional import InterpolationMode
+
+from trplib import apply_trp
+
+
+def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema=None, scaler=None):
+    model.train()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value}"))
+    metric_logger.add_meter("img/s", utils.SmoothedValue(window_size=10, fmt="{value}"))
+
+    header = f"Epoch: [{epoch}]"
+    for i, (image, target) in enumerate(metric_logger.log_every(data_loader, args.print_freq, header)):
+        start_time = time.time()
+        image, target = image.to(device), target.to(device)
+        with torch.amp.autocast("cuda", enabled=scaler is not None):
+            # output = model(image)
+            # loss = criterion(output, target)
+            output, loss = model(image, target)
+
+        optimizer.zero_grad()
+        if scaler is not None:
+            scaler.scale(loss).backward()
+            if args.clip_grad_norm is not None:
+                # we should unscale the gradients of optimizer's assigned params if do gradient clipping
+                scaler.unscale_(optimizer)
+                nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm)
+            scaler.step(optimizer)
+            scaler.update()
+        else:
+            loss.backward()
+            if args.clip_grad_norm is not None:
+                nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm)
+            optimizer.step()
+
+        if model_ema and i % args.model_ema_steps == 0:
+            model_ema.update_parameters(model)
+            if epoch < args.lr_warmup_epochs:
+                # Reset ema buffer to keep copying weights during warmup period
+                model_ema.n_averaged.fill_(0)
+
+        acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
+        batch_size = image.shape[0]
+        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
+        metric_logger.meters["acc1"].update(acc1.item(), n=batch_size)
+        metric_logger.meters["acc5"].update(acc5.item(), n=batch_size)
+        metric_logger.meters["img/s"].update(batch_size / (time.time() - start_time))
+
+
+def evaluate(model, criterion, data_loader, device, print_freq=100, log_suffix=""):
+    model.eval()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = f"Test: {log_suffix}"
+
+    num_processed_samples = 0
+    with torch.inference_mode():
+        for image, target in metric_logger.log_every(data_loader, print_freq, header):
+            image = image.to(device, non_blocking=True)
+            target = target.to(device, non_blocking=True)
+            output = model(image)
+            loss = criterion(output, target)
+
+            acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
+            # FIXME need to take into account that the datasets
+            # could have been padded in distributed setup
+            batch_size = image.shape[0]
+            metric_logger.update(loss=loss.item())
+            metric_logger.meters["acc1"].update(acc1.item(), n=batch_size)
+            metric_logger.meters["acc5"].update(acc5.item(), n=batch_size)
+            num_processed_samples += batch_size
+    # gather the stats from all processes
+
+    num_processed_samples = utils.reduce_across_processes(num_processed_samples)
+    if (
+        hasattr(data_loader.dataset, "__len__")
+        and len(data_loader.dataset) != num_processed_samples
+        and torch.distributed.get_rank() == 0
+    ):
+        # See FIXME above
+        warnings.warn(
+            f"It looks like the dataset has {len(data_loader.dataset)} samples, but {num_processed_samples} "
+            "samples were used for the validation, which might bias the results. "
+            "Try adjusting the batch size and / or the world size. "
+            "Setting the world size to 1 is always a safe bet."
+        )
+
+    metric_logger.synchronize_between_processes()
+
+    print(f"{header} Acc@1 {metric_logger.acc1.global_avg:.3f} Acc@5 {metric_logger.acc5.global_avg:.3f}")
+    return metric_logger.acc1.global_avg
+
+
+def _get_cache_path(filepath):
+    import hashlib
+
+    h = hashlib.sha1(filepath.encode()).hexdigest()
+    cache_path = os.path.join("~", ".torch", "vision", "datasets", "imagefolder", h[:10] + ".pt")
+    cache_path = os.path.expanduser(cache_path)
+    return cache_path
+
+
+def load_data(traindir, valdir, args):
+    # Data loading code
+    print("Loading data")
+    val_resize_size, val_crop_size, train_crop_size = (
+        args.val_resize_size,
+        args.val_crop_size,
+        args.train_crop_size,
+    )
+    interpolation = InterpolationMode(args.interpolation)
+
+    print("Loading training data")
+    st = time.time()
+    cache_path = _get_cache_path(traindir)
+    if args.cache_dataset and os.path.exists(cache_path):
+        # Attention, as the transforms are also cached!
+        print(f"Loading dataset_train from {cache_path}")
+        dataset, _ = torch.load(cache_path)
+    else:
+        auto_augment_policy = getattr(args, "auto_augment", None)
+        random_erase_prob = getattr(args, "random_erase", 0.0)
+        ra_magnitude = args.ra_magnitude
+        augmix_severity = args.augmix_severity
+        dataset = torchvision.datasets.ImageFolder(
+            traindir,
+            presets.ClassificationPresetTrain(
+                crop_size=train_crop_size,
+                interpolation=interpolation,
+                auto_augment_policy=auto_augment_policy,
+                random_erase_prob=random_erase_prob,
+                ra_magnitude=ra_magnitude,
+                augmix_severity=augmix_severity,
+            ),
+        )
+        if args.cache_dataset:
+            print(f"Saving dataset_train to {cache_path}")
+            utils.mkdir(os.path.dirname(cache_path))
+            utils.save_on_master((dataset, traindir), cache_path)
+    print("Took", time.time() - st)
+
+    print("Loading validation data")
+    cache_path = _get_cache_path(valdir)
+    if args.cache_dataset and os.path.exists(cache_path):
+        # Attention, as the transforms are also cached!
+        print(f"Loading dataset_test from {cache_path}")
+        dataset_test, _ = torch.load(cache_path)
+    else:
+        if args.weights and args.test_only:
+            weights = torchvision.models.get_weight(args.weights)
+            preprocessing = weights.transforms()
+        else:
+            preprocessing = presets.ClassificationPresetEval(
+                crop_size=val_crop_size, resize_size=val_resize_size, interpolation=interpolation
+            )
+
+        dataset_test = torchvision.datasets.ImageFolder(
+            valdir,
+            preprocessing,
+        )
+        if args.cache_dataset:
+            print(f"Saving dataset_test to {cache_path}")
+            utils.mkdir(os.path.dirname(cache_path))
+            utils.save_on_master((dataset_test, valdir), cache_path)
+
+    print("Creating data loaders")
+    if args.distributed:
+        if hasattr(args, "ra_sampler") and args.ra_sampler:
+            train_sampler = RASampler(dataset, shuffle=True, repetitions=args.ra_reps)
+        else:
+            train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test, shuffle=False)
+    else:
+        train_sampler = torch.utils.data.RandomSampler(dataset)
+        test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+
+    return dataset, dataset_test, train_sampler, test_sampler
+
+
+def main(args):
+    if args.output_dir:
+        utils.mkdir(args.output_dir)
+
+    utils.init_distributed_mode(args)
+    print(args)
+
+    device = torch.device(args.device)
+
+    if args.use_deterministic_algorithms:
+        torch.backends.cudnn.benchmark = False
+        torch.use_deterministic_algorithms(True)
+    else:
+        torch.backends.cudnn.benchmark = True
+
+    train_dir = os.path.join(args.data_path, "train")
+    val_dir = os.path.join(args.data_path, "val")
+    dataset, dataset_test, train_sampler, test_sampler = load_data(train_dir, val_dir, args)
+
+    collate_fn = None
+    num_classes = len(dataset.classes)
+    mixup_transforms = []
+    if args.mixup_alpha > 0.0:
+        mixup_transforms.append(transforms.RandomMixup(num_classes, p=1.0, alpha=args.mixup_alpha))
+    if args.cutmix_alpha > 0.0:
+        mixup_transforms.append(transforms.RandomCutmix(num_classes, p=1.0, alpha=args.cutmix_alpha))
+    if mixup_transforms:
+        mixupcutmix = torchvision.transforms.RandomChoice(mixup_transforms)
+
+        def collate_fn(batch):
+            return mixupcutmix(*default_collate(batch))
+
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        sampler=train_sampler,
+        num_workers=args.workers,
+        pin_memory=True,
+        collate_fn=collate_fn,
+    )
+    data_loader_test = torch.utils.data.DataLoader(
+        dataset_test, batch_size=8, sampler=test_sampler, num_workers=args.workers, pin_memory=True
+    )
+
+    print("Creating model")
+    model = torchvision.models.get_model(args.model, weights=args.weights, num_classes=num_classes)
+    if args.apply_trp:
+        model = apply_trp(model, args.trp_depths, args.trp_p, args.trp_lambdas, label_smoothing=args.label_smoothing)
+    model.to(device)
+
+    if args.distributed and args.sync_bn:
+        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+    criterion = nn.CrossEntropyLoss(label_smoothing=args.label_smoothing)
+
+    custom_keys_weight_decay = []
+    if args.bias_weight_decay is not None:
+        custom_keys_weight_decay.append(("bias", args.bias_weight_decay))
+    if args.transformer_embedding_decay is not None:
+        for key in ["class_token", "position_embedding", "relative_position_bias_table"]:
+            custom_keys_weight_decay.append((key, args.transformer_embedding_decay))
+    parameters = utils.set_weight_decay(
+        model,
+        args.weight_decay,
+        norm_weight_decay=args.norm_weight_decay,
+        custom_keys_weight_decay=custom_keys_weight_decay if len(custom_keys_weight_decay) > 0 else None,
+    )
+
+    opt_name = args.opt.lower()
+    if opt_name.startswith("sgd"):
+        optimizer = torch.optim.SGD(
+            parameters,
+            lr=args.lr,
+            momentum=args.momentum,
+            weight_decay=args.weight_decay,
+            nesterov="nesterov" in opt_name,
+        )
+    elif opt_name == "rmsprop":
+        optimizer = torch.optim.RMSprop(
+            parameters, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay, eps=0.0316, alpha=0.9
+        )
+    elif opt_name == "adamw":
+        optimizer = torch.optim.AdamW(parameters, lr=args.lr, weight_decay=args.weight_decay)
+    else:
+        raise RuntimeError(f"Invalid optimizer {args.opt}. Only SGD, RMSprop and AdamW are supported.")
+
+    scaler = torch.amp.GradScaler("cuda") if args.amp else None
+
+    args.lr_scheduler = args.lr_scheduler.lower()
+    if args.lr_scheduler == "steplr":
+        main_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
+    elif args.lr_scheduler == "cosineannealinglr":
+        main_lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+            optimizer, T_max=args.epochs - args.lr_warmup_epochs, eta_min=args.lr_min
+        )
+    elif args.lr_scheduler == "exponentiallr":
+        main_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=args.lr_gamma)
+    else:
+        raise RuntimeError(
+            f"Invalid lr scheduler '{args.lr_scheduler}'. Only StepLR, CosineAnnealingLR and ExponentialLR "
+            "are supported."
+        )
+
+    if args.lr_warmup_epochs > 0:
+        if args.lr_warmup_method == "linear":
+            warmup_lr_scheduler = torch.optim.lr_scheduler.LinearLR(
+                optimizer, start_factor=args.lr_warmup_decay, total_iters=args.lr_warmup_epochs
+            )
+        elif args.lr_warmup_method == "constant":
+            warmup_lr_scheduler = torch.optim.lr_scheduler.ConstantLR(
+                optimizer, factor=args.lr_warmup_decay, total_iters=args.lr_warmup_epochs
+            )
+        else:
+            raise RuntimeError(
+                f"Invalid warmup lr method '{args.lr_warmup_method}'. Only linear and constant are supported."
+            )
+        lr_scheduler = torch.optim.lr_scheduler.SequentialLR(
+            optimizer, schedulers=[warmup_lr_scheduler, main_lr_scheduler], milestones=[args.lr_warmup_epochs]
+        )
+    else:
+        lr_scheduler = main_lr_scheduler
+
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module
+
+    model_ema = None
+    if args.model_ema:
+        # Decay adjustment that aims to keep the decay independent from other hyper-parameters originally proposed at:
+        # https://github.com/facebookresearch/pycls/blob/f8cd9627/pycls/core/net.py#L123
+        #
+        # total_ema_updates = (Dataset_size / n_GPUs) * epochs / (batch_size_per_gpu * EMA_steps)
+        # We consider constant = Dataset_size for a given dataset/setup and ommit it. Thus:
+        # adjust = 1 / total_ema_updates ~= n_GPUs * batch_size_per_gpu * EMA_steps / epochs
+        adjust = args.world_size * args.batch_size * args.model_ema_steps / args.epochs
+        alpha = 1.0 - args.model_ema_decay
+        alpha = min(1.0, alpha * adjust)
+        model_ema = utils.ExponentialMovingAverage(model_without_ddp, device=device, decay=1.0 - alpha)
+
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=False)
+        model_without_ddp.load_state_dict(checkpoint["model"])
+        if not args.test_only:
+            optimizer.load_state_dict(checkpoint["optimizer"])
+            lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
+        args.start_epoch = checkpoint["epoch"] + 1
+        if model_ema:
+            model_ema.load_state_dict(checkpoint["model_ema"])
+        if scaler:
+            scaler.load_state_dict(checkpoint["scaler"])
+
+    if args.test_only:
+        # We disable the cudnn benchmarking because it can noticeably affect the accuracy
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+        if model_ema:
+            evaluate(model_ema, criterion, data_loader_test, device=device, log_suffix="EMA")
+        else:
+            evaluate(model, criterion, data_loader_test, device=device)
+        return
+
+    print("Start training")
+    start_time = time.time()
+    for epoch in range(args.start_epoch, args.epochs):
+        if args.distributed:
+            train_sampler.set_epoch(epoch)
+        train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema, scaler)
+        lr_scheduler.step()
+        evaluate(model, criterion, data_loader_test, device=device)
+        if model_ema:
+            evaluate(model_ema, criterion, data_loader_test, device=device, log_suffix="EMA")
+        if args.output_dir:
+            checkpoint = {
+                "model": model_without_ddp.state_dict() if not args.apply_trp else {k: v for k, v in model_without_ddp.state_dict().items() if not k.startswith("trp_blocks")},  # NOTE: remove TRP heads
+                "optimizer": optimizer.state_dict(),
+                "lr_scheduler": lr_scheduler.state_dict(),
+                "epoch": epoch,
+                "args": args,
+            }
+            if model_ema:
+                checkpoint["model_ema"] = model_ema.state_dict() if not args.apply_trp else {k: v for k, v in model_ema.state_dict().items() if not k.startswith("trp_blocks")}  # NOTE: remove TRP heads
+            if scaler:
+                checkpoint["scaler"] = scaler.state_dict()
+            utils.save_on_master(checkpoint, os.path.join(args.output_dir, f"model_{epoch}.pth"))
+            utils.save_on_master(checkpoint, os.path.join(args.output_dir, "checkpoint.pth"))
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print(f"Training time {total_time_str}")
+
+
+def get_args_parser(add_help=True):
+    import argparse
+
+    parser = argparse.ArgumentParser(description="PyTorch Classification Training", add_help=add_help)
+
+    parser.add_argument("--data-path", default="/datasets01/imagenet_full_size/061417/", type=str, help="dataset path")
+    parser.add_argument("--model", default="resnet18", type=str, help="model name")
+    parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
+    parser.add_argument(
+        "-b", "--batch-size", default=32, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
+    )
+    parser.add_argument("--epochs", default=90, type=int, metavar="N", help="number of total epochs to run")
+    parser.add_argument(
+        "-j", "--workers", default=16, type=int, metavar="N", help="number of data loading workers (default: 16)"
+    )
+    parser.add_argument("--opt", default="sgd", type=str, help="optimizer")
+    parser.add_argument("--lr", default=0.1, type=float, help="initial learning rate")
+    parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")
+    parser.add_argument(
+        "--wd",
+        "--weight-decay",
+        default=1e-4,
+        type=float,
+        metavar="W",
+        help="weight decay (default: 1e-4)",
+        dest="weight_decay",
+    )
+    parser.add_argument(
+        "--norm-weight-decay",
+        default=None,
+        type=float,
+        help="weight decay for Normalization layers (default: None, same value as --wd)",
+    )
+    parser.add_argument(
+        "--bias-weight-decay",
+        default=None,
+        type=float,
+        help="weight decay for bias parameters of all layers (default: None, same value as --wd)",
+    )
+    parser.add_argument(
+        "--transformer-embedding-decay",
+        default=None,
+        type=float,
+        help="weight decay for embedding parameters for vision transformer models (default: None, same value as --wd)",
+    )
+    parser.add_argument(
+        "--label-smoothing", default=0.0, type=float, help="label smoothing (default: 0.0)", dest="label_smoothing"
+    )
+    parser.add_argument("--mixup-alpha", default=0.0, type=float, help="mixup alpha (default: 0.0)")
+    parser.add_argument("--cutmix-alpha", default=0.0, type=float, help="cutmix alpha (default: 0.0)")
+    parser.add_argument("--lr-scheduler", default="steplr", type=str, help="the lr scheduler (default: steplr)")
+    parser.add_argument("--lr-warmup-epochs", default=0, type=int, help="the number of epochs to warmup (default: 0)")
+    parser.add_argument(
+        "--lr-warmup-method", default="constant", type=str, help="the warmup method (default: constant)"
+    )
+    parser.add_argument("--lr-warmup-decay", default=0.01, type=float, help="the decay for lr")
+    parser.add_argument("--lr-step-size", default=30, type=int, help="decrease lr every step-size epochs")
+    parser.add_argument("--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma")
+    parser.add_argument("--lr-min", default=0.0, type=float, help="minimum lr of lr schedule (default: 0.0)")
+    parser.add_argument("--print-freq", default=10, type=int, help="print frequency")
+    parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
+    parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
+    parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")
+    parser.add_argument(
+        "--cache-dataset",
+        dest="cache_dataset",
+        help="Cache the datasets for quicker initialization. It also serializes the transforms",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--sync-bn",
+        dest="sync_bn",
+        help="Use sync batch norm",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--test-only",
+        dest="test_only",
+        help="Only test the model",
+        action="store_true",
+    )
+    parser.add_argument("--auto-augment", default=None, type=str, help="auto augment policy (default: None)")
+    parser.add_argument("--ra-magnitude", default=9, type=int, help="magnitude of auto augment policy")
+    parser.add_argument("--augmix-severity", default=3, type=int, help="severity of augmix policy")
+    parser.add_argument("--random-erase", default=0.0, type=float, help="random erasing probability (default: 0.0)")
+
+    # Mixed precision training parameters
+    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
+
+    # distributed training parameters
+    parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
+    parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
+    parser.add_argument(
+        "--model-ema", action="store_true", help="enable tracking Exponential Moving Average of model parameters"
+    )
+    parser.add_argument(
+        "--model-ema-steps",
+        type=int,
+        default=32,
+        help="the number of iterations that controls how often to update the EMA model (default: 32)",
+    )
+    parser.add_argument(
+        "--model-ema-decay",
+        type=float,
+        default=0.99998,
+        help="decay factor for Exponential Moving Average of model parameters (default: 0.99998)",
+    )
+    parser.add_argument(
+        "--use-deterministic-algorithms", action="store_true", help="Forces the use of deterministic algorithms only."
+    )
+    parser.add_argument(
+        "--interpolation", default="bilinear", type=str, help="the interpolation method (default: bilinear)"
+    )
+    parser.add_argument(
+        "--val-resize-size", default=256, type=int, help="the resize size used for validation (default: 256)"
+    )
+    parser.add_argument(
+        "--val-crop-size", default=224, type=int, help="the central crop size used for validation (default: 224)"
+    )
+    parser.add_argument(
+        "--train-crop-size", default=224, type=int, help="the random crop size used for training (default: 224)"
+    )
+    parser.add_argument("--clip-grad-norm", default=None, type=float, help="the maximum gradient norm (default None)")
+    parser.add_argument("--ra-sampler", action="store_true", help="whether to use Repeated Augmentation in training")
+    parser.add_argument(
+        "--ra-reps", default=3, type=int, help="number of repetitions for Repeated Augmentation (default: 3)"
+    )
+    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
+
+    parser.add_argument("--apply-trp", action="store_true", help="enable applying trp")
+    parser.add_argument("--trp-depths", type=int, help="trp depth")
+    parser.add_argument("--trp-p", type=float, help="trp p")
+    parser.add_argument("--trp-lambdas", nargs="+", type=float, help="trp lambdas")
+
+    return parser
+
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/hpo-examples/image-classification/train_quantization.py b/hpo-examples/image-classification/train_quantization.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed36e13a028ac2f07d26f7d7e8ab8573744e9fde
--- /dev/null
+++ b/hpo-examples/image-classification/train_quantization.py
@@ -0,0 +1,265 @@
+import copy
+import datetime
+import os
+import time
+
+import torch
+import torch.ao.quantization
+import torch.utils.data
+import torchvision
+import utils
+from torch import nn
+from train import evaluate, load_data, train_one_epoch
+
+
+def main(args):
+    if args.output_dir:
+        utils.mkdir(args.output_dir)
+
+    utils.init_distributed_mode(args)
+    print(args)
+
+    if args.post_training_quantize and args.distributed:
+        raise RuntimeError("Post training quantization example should not be performed on distributed mode")
+
+    # Set backend engine to ensure that quantized model runs on the correct kernels
+    if args.backend not in torch.backends.quantized.supported_engines:
+        raise RuntimeError("Quantized backend not supported: " + str(args.backend))
+    torch.backends.quantized.engine = args.backend
+
+    device = torch.device(args.device)
+    torch.backends.cudnn.benchmark = True
+
+    # Data loading code
+    print("Loading data")
+    train_dir = os.path.join(args.data_path, "train")
+    val_dir = os.path.join(args.data_path, "val")
+
+    dataset, dataset_test, train_sampler, test_sampler = load_data(train_dir, val_dir, args)
+    data_loader = torch.utils.data.DataLoader(
+        dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=args.workers, pin_memory=True
+    )
+
+    data_loader_test = torch.utils.data.DataLoader(
+        dataset_test, batch_size=args.eval_batch_size, sampler=test_sampler, num_workers=args.workers, pin_memory=True
+    )
+
+    print("Creating model", args.model)
+    # when training quantized models, we always start from a pre-trained fp32 reference model
+    prefix = "quantized_"
+    model_name = args.model
+    if not model_name.startswith(prefix):
+        model_name = prefix + model_name
+    model = torchvision.models.get_model(model_name, weights=args.weights, quantize=args.test_only)
+    model.to(device)
+
+    if not (args.test_only or args.post_training_quantize):
+        model.fuse_model(is_qat=True)
+        model.qconfig = torch.ao.quantization.get_default_qat_qconfig(args.backend)
+        torch.ao.quantization.prepare_qat(model, inplace=True)
+
+        if args.distributed and args.sync_bn:
+            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+        optimizer = torch.optim.SGD(
+            model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay
+        )
+
+        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
+
+    criterion = nn.CrossEntropyLoss()
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module
+
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location="cpu")
+        model_without_ddp.load_state_dict(checkpoint["model"])
+        optimizer.load_state_dict(checkpoint["optimizer"])
+        lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
+        args.start_epoch = checkpoint["epoch"] + 1
+
+    if args.post_training_quantize:
+        # perform calibration on a subset of the training dataset
+        # for that, create a subset of the training dataset
+        ds = torch.utils.data.Subset(dataset, indices=list(range(args.batch_size * args.num_calibration_batches)))
+        data_loader_calibration = torch.utils.data.DataLoader(
+            ds, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True
+        )
+        model.eval()
+        model.fuse_model(is_qat=False)
+        model.qconfig = torch.ao.quantization.get_default_qconfig(args.backend)
+        torch.ao.quantization.prepare(model, inplace=True)
+        # Calibrate first
+        print("Calibrating")
+        evaluate(model, criterion, data_loader_calibration, device=device, print_freq=1)
+        torch.ao.quantization.convert(model, inplace=True)
+        if args.output_dir:
+            print("Saving quantized model")
+            if utils.is_main_process():
+                torch.save(model.state_dict(), os.path.join(args.output_dir, "quantized_post_train_model.pth"))
+        print("Evaluating post-training quantized model")
+        evaluate(model, criterion, data_loader_test, device=device)
+        return
+
+    if args.test_only:
+        evaluate(model, criterion, data_loader_test, device=device)
+        return
+
+    model.apply(torch.ao.quantization.enable_observer)
+    model.apply(torch.ao.quantization.enable_fake_quant)
+    start_time = time.time()
+    for epoch in range(args.start_epoch, args.epochs):
+        if args.distributed:
+            train_sampler.set_epoch(epoch)
+        print("Starting training for epoch", epoch)
+        train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args)
+        lr_scheduler.step()
+        with torch.inference_mode():
+            if epoch >= args.num_observer_update_epochs:
+                print("Disabling observer for subseq epochs, epoch = ", epoch)
+                model.apply(torch.ao.quantization.disable_observer)
+            if epoch >= args.num_batch_norm_update_epochs:
+                print("Freezing BN for subseq epochs, epoch = ", epoch)
+                model.apply(torch.nn.intrinsic.qat.freeze_bn_stats)
+            print("Evaluate QAT model")
+
+            evaluate(model, criterion, data_loader_test, device=device, log_suffix="QAT")
+            quantized_eval_model = copy.deepcopy(model_without_ddp)
+            quantized_eval_model.eval()
+            quantized_eval_model.to(torch.device("cpu"))
+            torch.ao.quantization.convert(quantized_eval_model, inplace=True)
+
+            print("Evaluate Quantized model")
+            evaluate(quantized_eval_model, criterion, data_loader_test, device=torch.device("cpu"))
+
+        model.train()
+
+        if args.output_dir:
+            checkpoint = {
+                "model": model_without_ddp.state_dict(),
+                "eval_model": quantized_eval_model.state_dict(),
+                "optimizer": optimizer.state_dict(),
+                "lr_scheduler": lr_scheduler.state_dict(),
+                "epoch": epoch,
+                "args": args,
+            }
+            utils.save_on_master(checkpoint, os.path.join(args.output_dir, f"model_{epoch}.pth"))
+            utils.save_on_master(checkpoint, os.path.join(args.output_dir, "checkpoint.pth"))
+        print("Saving models after epoch ", epoch)
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print(f"Training time {total_time_str}")
+
+
+def get_args_parser(add_help=True):
+    import argparse
+
+    parser = argparse.ArgumentParser(description="PyTorch Quantized Classification Training", add_help=add_help)
+
+    parser.add_argument("--data-path", default="/datasets01/imagenet_full_size/061417/", type=str, help="dataset path")
+    parser.add_argument("--model", default="mobilenet_v2", type=str, help="model name")
+    parser.add_argument("--backend", default="qnnpack", type=str, help="fbgemm or qnnpack")
+    parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
+
+    parser.add_argument(
+        "-b", "--batch-size", default=32, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
+    )
+    parser.add_argument("--eval-batch-size", default=128, type=int, help="batch size for evaluation")
+    parser.add_argument("--epochs", default=90, type=int, metavar="N", help="number of total epochs to run")
+    parser.add_argument(
+        "--num-observer-update-epochs",
+        default=4,
+        type=int,
+        metavar="N",
+        help="number of total epochs to update observers",
+    )
+    parser.add_argument(
+        "--num-batch-norm-update-epochs",
+        default=3,
+        type=int,
+        metavar="N",
+        help="number of total epochs to update batch norm stats",
+    )
+    parser.add_argument(
+        "--num-calibration-batches",
+        default=32,
+        type=int,
+        metavar="N",
+        help="number of batches of training set for \
+                              observer calibration ",
+    )
+
+    parser.add_argument(
+        "-j", "--workers", default=16, type=int, metavar="N", help="number of data loading workers (default: 16)"
+    )
+    parser.add_argument("--lr", default=0.0001, type=float, help="initial learning rate")
+    parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")
+    parser.add_argument(
+        "--wd",
+        "--weight-decay",
+        default=1e-4,
+        type=float,
+        metavar="W",
+        help="weight decay (default: 1e-4)",
+        dest="weight_decay",
+    )
+    parser.add_argument("--lr-step-size", default=30, type=int, help="decrease lr every step-size epochs")
+    parser.add_argument("--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma")
+    parser.add_argument("--print-freq", default=10, type=int, help="print frequency")
+    parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
+    parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
+    parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")
+    parser.add_argument(
+        "--cache-dataset",
+        dest="cache_dataset",
+        help="Cache the datasets for quicker initialization. \
+             It also serializes the transforms",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--sync-bn",
+        dest="sync_bn",
+        help="Use sync batch norm",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--test-only",
+        dest="test_only",
+        help="Only test the model",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--post-training-quantize",
+        dest="post_training_quantize",
+        help="Post training quantize the model",
+        action="store_true",
+    )
+
+    # distributed training parameters
+    parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
+    parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
+
+    parser.add_argument(
+        "--interpolation", default="bilinear", type=str, help="the interpolation method (default: bilinear)"
+    )
+    parser.add_argument(
+        "--val-resize-size", default=256, type=int, help="the resize size used for validation (default: 256)"
+    )
+    parser.add_argument(
+        "--val-crop-size", default=224, type=int, help="the central crop size used for validation (default: 224)"
+    )
+    parser.add_argument(
+        "--train-crop-size", default=224, type=int, help="the random crop size used for training (default: 224)"
+    )
+    parser.add_argument("--clip-grad-norm", default=None, type=float, help="the maximum gradient norm (default None)")
+    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
+
+    return parser
+
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/hpo-examples/image-classification/transforms.py b/hpo-examples/image-classification/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a8ef7877d6d5525aead106695d495b7a05cb009
--- /dev/null
+++ b/hpo-examples/image-classification/transforms.py
@@ -0,0 +1,183 @@
+import math
+from typing import Tuple
+
+import torch
+from torch import Tensor
+from torchvision.transforms import functional as F
+
+
+class RandomMixup(torch.nn.Module):
+    """Randomly apply Mixup to the provided batch and targets.
+    The class implements the data augmentations as described in the paper
+    `"mixup: Beyond Empirical Risk Minimization" <https://arxiv.org/abs/1710.09412>`_.
+
+    Args:
+        num_classes (int): number of classes used for one-hot encoding.
+        p (float): probability of the batch being transformed. Default value is 0.5.
+        alpha (float): hyperparameter of the Beta distribution used for mixup.
+            Default value is 1.0.
+        inplace (bool): boolean to make this transform inplace. Default set to False.
+    """
+
+    def __init__(self, num_classes: int, p: float = 0.5, alpha: float = 1.0, inplace: bool = False) -> None:
+        super().__init__()
+
+        if num_classes < 1:
+            raise ValueError(
+                f"Please provide a valid positive value for the num_classes. Got num_classes={num_classes}"
+            )
+
+        if alpha <= 0:
+            raise ValueError("Alpha param can't be zero.")
+
+        self.num_classes = num_classes
+        self.p = p
+        self.alpha = alpha
+        self.inplace = inplace
+
+    def forward(self, batch: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            batch (Tensor): Float tensor of size (B, C, H, W)
+            target (Tensor): Integer tensor of size (B, )
+
+        Returns:
+            Tensor: Randomly transformed batch.
+        """
+        if batch.ndim != 4:
+            raise ValueError(f"Batch ndim should be 4. Got {batch.ndim}")
+        if target.ndim != 1:
+            raise ValueError(f"Target ndim should be 1. Got {target.ndim}")
+        if not batch.is_floating_point():
+            raise TypeError(f"Batch dtype should be a float tensor. Got {batch.dtype}.")
+        if target.dtype != torch.int64:
+            raise TypeError(f"Target dtype should be torch.int64. Got {target.dtype}")
+
+        if not self.inplace:
+            batch = batch.clone()
+            target = target.clone()
+
+        if target.ndim == 1:
+            target = torch.nn.functional.one_hot(target, num_classes=self.num_classes).to(dtype=batch.dtype)
+
+        if torch.rand(1).item() >= self.p:
+            return batch, target
+
+        # It's faster to roll the batch by one instead of shuffling it to create image pairs
+        batch_rolled = batch.roll(1, 0)
+        target_rolled = target.roll(1, 0)
+
+        # Implemented as on mixup paper, page 3.
+        lambda_param = float(torch._sample_dirichlet(torch.tensor([self.alpha, self.alpha]))[0])
+        batch_rolled.mul_(1.0 - lambda_param)
+        batch.mul_(lambda_param).add_(batch_rolled)
+
+        target_rolled.mul_(1.0 - lambda_param)
+        target.mul_(lambda_param).add_(target_rolled)
+
+        return batch, target
+
+    def __repr__(self) -> str:
+        s = (
+            f"{self.__class__.__name__}("
+            f"num_classes={self.num_classes}"
+            f", p={self.p}"
+            f", alpha={self.alpha}"
+            f", inplace={self.inplace}"
+            f")"
+        )
+        return s
+
+
+class RandomCutmix(torch.nn.Module):
+    """Randomly apply Cutmix to the provided batch and targets.
+    The class implements the data augmentations as described in the paper
+    `"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features"
+    <https://arxiv.org/abs/1905.04899>`_.
+
+    Args:
+        num_classes (int): number of classes used for one-hot encoding.
+        p (float): probability of the batch being transformed. Default value is 0.5.
+        alpha (float): hyperparameter of the Beta distribution used for cutmix.
+            Default value is 1.0.
+        inplace (bool): boolean to make this transform inplace. Default set to False.
+    """
+
+    def __init__(self, num_classes: int, p: float = 0.5, alpha: float = 1.0, inplace: bool = False) -> None:
+        super().__init__()
+        if num_classes < 1:
+            raise ValueError("Please provide a valid positive value for the num_classes.")
+        if alpha <= 0:
+            raise ValueError("Alpha param can't be zero.")
+
+        self.num_classes = num_classes
+        self.p = p
+        self.alpha = alpha
+        self.inplace = inplace
+
+    def forward(self, batch: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            batch (Tensor): Float tensor of size (B, C, H, W)
+            target (Tensor): Integer tensor of size (B, )
+
+        Returns:
+            Tensor: Randomly transformed batch.
+        """
+        if batch.ndim != 4:
+            raise ValueError(f"Batch ndim should be 4. Got {batch.ndim}")
+        if target.ndim != 1:
+            raise ValueError(f"Target ndim should be 1. Got {target.ndim}")
+        if not batch.is_floating_point():
+            raise TypeError(f"Batch dtype should be a float tensor. Got {batch.dtype}.")
+        if target.dtype != torch.int64:
+            raise TypeError(f"Target dtype should be torch.int64. Got {target.dtype}")
+
+        if not self.inplace:
+            batch = batch.clone()
+            target = target.clone()
+
+        if target.ndim == 1:
+            target = torch.nn.functional.one_hot(target, num_classes=self.num_classes).to(dtype=batch.dtype)
+
+        if torch.rand(1).item() >= self.p:
+            return batch, target
+
+        # It's faster to roll the batch by one instead of shuffling it to create image pairs
+        batch_rolled = batch.roll(1, 0)
+        target_rolled = target.roll(1, 0)
+
+        # Implemented as on cutmix paper, page 12 (with minor corrections on typos).
+        lambda_param = float(torch._sample_dirichlet(torch.tensor([self.alpha, self.alpha]))[0])
+        _, H, W = F.get_dimensions(batch)
+
+        r_x = torch.randint(W, (1,))
+        r_y = torch.randint(H, (1,))
+
+        r = 0.5 * math.sqrt(1.0 - lambda_param)
+        r_w_half = int(r * W)
+        r_h_half = int(r * H)
+
+        x1 = int(torch.clamp(r_x - r_w_half, min=0))
+        y1 = int(torch.clamp(r_y - r_h_half, min=0))
+        x2 = int(torch.clamp(r_x + r_w_half, max=W))
+        y2 = int(torch.clamp(r_y + r_h_half, max=H))
+
+        batch[:, :, y1:y2, x1:x2] = batch_rolled[:, :, y1:y2, x1:x2]
+        lambda_param = float(1.0 - (x2 - x1) * (y2 - y1) / (W * H))
+
+        target_rolled.mul_(1.0 - lambda_param)
+        target.mul_(lambda_param).add_(target_rolled)
+
+        return batch, target
+
+    def __repr__(self) -> str:
+        s = (
+            f"{self.__class__.__name__}("
+            f"num_classes={self.num_classes}"
+            f", p={self.p}"
+            f", alpha={self.alpha}"
+            f", inplace={self.inplace}"
+            f")"
+        )
+        return s
diff --git a/hpo-examples/image-classification/trplib.py b/hpo-examples/image-classification/trplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..905b01abb834d546aaa26518f4a372b4ed0a429a
--- /dev/null
+++ b/hpo-examples/image-classification/trplib.py
@@ -0,0 +1,1181 @@
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from torchvision.models.mobilenetv2 import MobileNetV2
+from torchvision.models.resnet import ResNet
+from torchvision.models.efficientnet import EfficientNet
+from torchvision.models.vision_transformer import VisionTransformer
+from torchvision.models.segmentation.fcn import FCN
+from torchvision.models.segmentation.deeplabv3 import DeepLabV3
+
+import transformers
+from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, CausalLMOutput, Seq2SeqLMOutput
+
+from typing import Optional, Tuple, List, Union, Callable
+from collections import OrderedDict
+import types
+
+
+def trp_criterion(trp_blocks: nn.ModuleList, shared_head: Callable, criterion: Callable, lambdas: List[float], hidden_states: Tensor, logits: Tensor, targets: Tensor, loss_normalization=False):
+    loss, mask = criterion(logits, targets)
+    if loss_normalization:
+        coeff = loss.detach()
+
+    embeds = [hidden_states]
+    predictions = []
+    for k, c in enumerate(lambdas):
+        embeds.append(trp_blocks[k](embeds[-1]))
+        predictions.append(shared_head(embeds[-1]))
+        replica_loss, mask = criterion(predictions[-1], targets, mask)
+        loss += c * replica_loss
+    
+    if loss_normalization:
+        with torch.no_grad():
+            coeff = torch.exp(coeff) / torch.exp(loss.detach())
+        loss = coeff * loss
+    
+    return loss
+
+
+class TPBlock(nn.Module):
+    def __init__(self, depths: int, in_features: int, p: float, dim=-1):
+        super(TPBlock, self).__init__()
+
+        self.dropout = nn.Dropout(p)
+
+        self.cdim = dim
+
+        blocks = []
+        for _ in range(depths):
+            blocks.append(nn.Linear(in_features, in_features))
+            nn.init.constant_(blocks[-1].weight, 0.0)
+            nn.init.constant_(blocks[-1].bias, 0.0)
+            blocks.append(nn.ReLU())
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        x = self.dropout(x)
+        if self.cdim == -1:
+            x = x + self.blocks(x)
+        else:
+            x = x + torch.movedim(self.blocks(torch.movedim(x, self.cdim, -1)), -1, self.cdim)
+        return x
+    
+
+class Config:
+    @staticmethod
+    def gen_criterion(*args, **kwargs):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor.
+                target (Tensor): Target labels.
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor with the same shape of target.
+            """
+            pass
+        return func
+    
+    @staticmethod
+    def gen_shared_head(*args, **kwargs):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States tensor.
+
+            Returns:
+                logits (Tensor): Logits tensor.
+            """
+            pass
+        return func
+
+    @staticmethod
+    def forward(*args, **kwargs):
+        pass
+
+
+# Wav2Vec2 for Audio Classification
+class Wav2Vec2ForSequenceClassificationConfig(Config): 
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self, attention_mask):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+            output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            if self.config.use_weighted_layer_sum:
+                hidden_states = outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION]
+                hidden_states = torch.stack(hidden_states, dim=1)
+                norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+                hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+            else:
+                hidden_states = outputs[0]
+
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                shared_head = Wav2Vec2ForSequenceClassificationConfig.gen_shared_head(self, attention_mask)
+                criterion = Wav2Vec2ForSequenceClassificationConfig.gen_criterion()
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states, logits.view(-1, self.config.num_labels),  labels.view(-1), loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# MobileNetV2 for Image Classification
+class MobileNetV2Config(Config):
+    @staticmethod
+    def gen_criterion(label_smoothing=0.0, top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B] or [B, C].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            label = torch.argmax(target, dim=1) if label_smoothing > 0.0 else target
+                
+            unmasked_loss = F.cross_entropy(input, label, reduction="none", label_smoothing=label_smoothing)
+            if mask is None:
+                mask = torch.ones_like(unmasked_loss, dtype=torch.float32, device=target.device)
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=-1)
+                mask = mask * torch.eq(topk_indices, label[:, None]).any(dim=-1).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = MobileNetV2Config.gen_shared_head(self)
+                criterion = MobileNetV2Config.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ResNet for Image Classification
+class ResNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.fc(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.conv1(images)
+            x = self.bn1(x)
+            x = self.relu(x)
+            x = self.maxpool(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.fc(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = ResNetConfig.gen_shared_head(self)
+                criterion = ResNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# EfficientNet for Image Classification
+class EfficientNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = EfficientNetConfig.gen_shared_head(self)
+                criterion = EfficientNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ViT for Image Classification
+class VisionTransformerConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.heads(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self._process_input(images)
+            n = x.shape[0]
+            batch_class_token = self.class_token.expand(n, -1, -1)
+            x = torch.cat([batch_class_token, x], dim=1)
+            x = self.encoder(x)
+            x = x[:, 0]
+
+            logits = self.heads(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = VisionTransformerConfig.gen_shared_head(self)
+                criterion = VisionTransformerConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# Bert for Question Answering
+class BertForQuestionAnsweringConfig(Config): 
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target: List[Tensor], mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C, 2].
+                target (List[Tensor]): 
+                    Start Positions of shape [B].
+                    End Positions of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            start_positions, end_positions = target
+
+            if mask is None:
+                mask = torch.ones_like(start_positions, dtype=torch.float32, device=start_positions.device)
+
+            start_logits, end_logits = input.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            masked_start_losses = F.cross_entropy(start_logits, start_positions, ignore_index=ignored_index, reduction="none")
+            start_loss = torch.sum(mask * masked_start_losses) / (torch.sum(mask) + 1e-6)
+            masked_end_losses = F.cross_entropy(end_logits, end_positions, ignore_index=ignored_index, reduction="none")
+            end_loss = torch.sum(mask * masked_end_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(start_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, start_positions[:, None]).any(dim=1).to(start_logits.dtype)
+                topk_values, topk_indices = torch.topk(end_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, end_positions[:, None]).any(dim=1).to(end_logits.dtype)
+
+            return (start_loss + end_loss) / 2, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.qa_outputs(hidden_states)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            start_positions: Optional[torch.Tensor] = None,
+            end_positions: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+            r"""
+            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the start of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the end of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            sequence_output = outputs[0]
+
+            logits = self.qa_outputs(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                shared_head = BertForQuestionAnsweringConfig.gen_shared_head(self)
+                criterion = BertForQuestionAnsweringConfig.gen_criterion()
+                total_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, [start_positions, end_positions], loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+    
+
+# FCN for Semantic Segmentation
+class FCNConfig(Config):
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input Tensor: input tensor of shape [B, C, H, W].
+                target (Tensor): Target labels of shape [B, H, W].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B, H, W].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+                
+            masked_loss = F.cross_entropy(input, target, ignore_index=255, reduction="none")
+            loss = torch.sum(mask * masked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, target[:, None, :, :]).any(dim=1).to(input.dtype)
+                # mask = mask * torch.eq(torch.argmax(x, dim=1), target).to(x.dtype)
+                
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_out_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+    
+    @staticmethod
+    def gen_aux_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.aux_classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            input_shape = images.shape[-2:]
+            # contract: features is a dict of tensors
+            features = self.backbone(images)
+
+            result = OrderedDict()
+            x = features["out"]
+            x = self.classifier(x)
+            x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            result["out"] = x
+
+            if self.aux_classifier is not None:
+                x = features["aux"]
+                x = self.aux_classifier(x)
+                x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+                result["aux"] = x
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                out_shared_head = FCNConfig.gen_out_shared_head(self, input_shape)
+                aux_shared_head = FCNConfig.gen_aux_shared_head(self, input_shape)
+                criterion = FCNConfig.gen_criterion(top_k)
+                out_loss = trp_criterion(self.out_trp_blocks, out_shared_head, criterion, lambdas, features["out"], result["out"], targets, loss_normalization)
+                aux_loss = trp_criterion(self.aux_trp_blocks, aux_shared_head, criterion, lambdas, features["aux"], result["aux"], targets, loss_normalization)
+                loss = out_loss + 0.5 * aux_loss
+                return result, loss
+            return result
+        return func
+
+
+# DeepLabV3Config for Semantic Segmentation
+class DeepLabV3Config(FCNConfig):
+    pass
+
+
+# Bert for Text Classification
+class BertForSequenceClassificationConfig(Config): 
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, pooled_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Boberta for Text Classification
+class RobertaForSequenceClassificationConfig(BertForSequenceClassificationConfig): 
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.roberta(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            sequence_output = outputs[0]
+            logits = self.classifier(sequence_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                # move labels to correct device to enable model parallelism
+                labels = labels.to(logits.device)
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Wav2Vec2 for Speech Recognition
+class Wav2Vec2ForCTCConfig(Config):
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def greedy_decode_ctc(
+        log_probs: torch.Tensor,
+        input_lengths: torch.Tensor,
+        blank_token_id: int,
+        target_lengths: torch.Tensor
+    ):
+        """
+        Convert logits to flattened predictions that match the shape of flattened_targets.
+
+        Args:
+            log_probs: [B, L, V] - log-softmax output
+            input_lengths: [B] - actual length of each input
+            blank_token_id: int - index of blank token
+            target_lengths: [B] - used to determine how many predictions to keep per sample
+
+        Returns:
+            flattened_predictions: 1D tensor, same total length as sum(target_lengths)
+        """
+        batch_size = log_probs.size(0)
+        decoded_all = []
+
+        predicted_ids = log_probs.argmax(dim=-1)  # [B, L]
+
+        for i in range(batch_size):
+            pred = predicted_ids[i][:input_lengths[i]]  # [Li]
+            prev = None
+            decoded = []
+            for token in pred:
+                token = token.item()
+                if token != blank_token_id and token != prev:
+                    decoded.append(token)
+                prev = token
+            # Trim or pad to match target_lengths[i]
+            tgt_len = target_lengths[i].item()
+            if len(decoded) >= tgt_len:
+                decoded = decoded[:tgt_len]
+            else:
+                decoded = decoded + [blank_token_id] * (tgt_len - len(decoded))  # pad with blank
+            decoded_all.extend(decoded)
+
+        return torch.tensor(decoded_all, dtype=torch.long, device=log_probs.device)  # shape: [sum(target_lengths)]
+
+    @staticmethod
+    def gen_criterion(input_lengths: Tensor, pad_token_id: int, ctc_zero_infinity: bool):
+        def func(logits: Tensor, labels: Tensor, mask=None):
+            """
+            Args:
+                logits (Tensor): Log Probablities of shape [B, L, V].
+                labels (Tensor): Flattened Targets of shape [B, L'].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(input_lengths, dtype=torch.float32, device=input_lengths.device)
+
+            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            with torch.backends.cudnn.flags(enabled=False):
+                masked_losses = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=pad_token_id, reduction="none", zero_infinity=ctc_zero_infinity)
+                loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                thres = 0.5
+                flattened_predictions = Wav2Vec2ForCTCConfig.greedy_decode_ctc(
+                    log_probs.transpose(0, 1),  # [B, T, V]
+                    input_lengths=input_lengths,
+                    blank_token_id=pad_token_id,
+                    target_lengths=target_lengths
+                )
+                token_wise_mask =  torch.eq(flattened_predictions, flattened_targets).to(flattened_targets.dtype)
+                segment_ids = torch.arange(len(target_lengths), device=target_lengths.device).repeat_interleave(target_lengths)
+                sequence_wise_mask = torch.zeros(len(target_lengths), dtype=target_lengths.dtype, device=token_wise_mask.device).scatter_add(0, segment_ids, token_wise_mask)
+                mask = mask * torch.ge(sequence_wise_mask, thres * target_lengths).to(flattened_targets.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.lm_head(hidden_states)
+            # log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, CausalLMOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
+                Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
+                the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
+                All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
+                config.vocab_size - 1]`.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None and labels.max() >= self.config.vocab_size:
+                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            hidden_states = self.dropout(hidden_states)
+
+            logits = self.lm_head(hidden_states)
+
+            loss = None
+            if labels is not None:
+                # retrieve loss input_lengths from attention_mask
+                attention_mask = (
+                    attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
+                )
+                input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+                shared_head = Wav2Vec2ForCTCConfig.gen_shared_head(self)
+                criterion = Wav2Vec2ForCTCConfig.gen_criterion(input_lengths, self.config.pad_token_id, self.config.ctc_zero_infinity)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states,  logits, labels, loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForCTCConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return CausalLMOutput(
+                loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+            )
+        return func
+
+
+# MBart for Translation
+class MBartForConditionalGenerationConfig(Config): 
+    @staticmethod
+    def gen_criterion(vocab_size: int, top_k=1):
+        def func(logits, labels, mask=None):
+            """
+            Args:
+                logits (Tensor): Logits tensor of shape [B, L, V].
+                labels (Tensor): Target labels of shape [B, L].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(labels.view(-1), dtype=torch.float32, device=labels.device)
+                
+            masked_losses = F.cross_entropy(logits.view(-1, vocab_size), labels.view(-1), reduction="none")
+            loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(logits.view(-1, vocab_size), top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, labels.view(-1, 1)).any(dim=1).to(logits.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, L].
+            """
+            logits = self.lm_head(hidden_states) + self.final_logits_bias
+            return logits
+        return func
+    
+    @staticmethod
+    def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+        """
+        Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
+        have a single `decoder_start_token_id` in contrast to other Bart-like models.
+        """
+        prev_output_tokens = input_ids.clone()
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+        index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+        decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+        prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+        prev_output_tokens[:, 0] = decoder_start_tokens
+
+        return prev_output_tokens
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            Returns:
+
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None:
+                # if use_cache:
+                #     logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+                use_cache = False
+                if decoder_input_ids is None and decoder_inputs_embeds is None:
+                    decoder_input_ids = MBartForConditionalGenerationConfig.shift_tokens_right(labels, self.config.pad_token_id)
+
+            outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=decoder_input_ids,
+                encoder_outputs=encoder_outputs,
+                decoder_attention_mask=decoder_attention_mask,
+                head_mask=head_mask,
+                decoder_head_mask=decoder_head_mask,
+                cross_attn_head_mask=cross_attn_head_mask,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+            masked_lm_loss = None
+            if labels is not None:
+                shared_head = MBartForConditionalGenerationConfig.gen_shared_head(self)
+                criterion = MBartForConditionalGenerationConfig.gen_criterion(self.config.vocab_size)
+                masked_lm_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, outputs[0],  lm_logits, labels, loss_normalization)
+
+            if not return_dict:
+                output = (lm_logits,) + outputs[1:]
+                return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+            return Seq2SeqLMOutput(
+                loss=masked_lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                decoder_hidden_states=outputs.decoder_hidden_states,
+                decoder_attentions=outputs.decoder_attentions,
+                cross_attentions=outputs.cross_attentions,
+                encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+                encoder_hidden_states=outputs.encoder_hidden_states,
+                encoder_attentions=outputs.encoder_attentions,
+            )
+        return func
+
+
+def apply_trp(model, depths: int, p: float, lambdas: List[float], **kwargs):
+    if isinstance(model, transformers.Wav2Vec2ForSequenceClassification):
+        print("✅ Applying TRP to Wav2Vec2 for Audio Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, MobileNetV2):
+        print("✅ Applying TRP to MobileNetV2 for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(MobileNetV2Config.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, ResNet):
+        print("✅ Applying TRP to ResNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p) for _ in lambdas])
+        model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, EfficientNet):
+        print("✅ Applying TRP to EfficientNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(EfficientNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, VisionTransformer):
+        print("✅ Applying TRP to VisionTransformer for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(VisionTransformerConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, transformers.BertForQuestionAnswering):
+        print("✅ Applying TRP to Bert for Question Answering...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForQuestionAnsweringConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, FCN):
+        print("✅ Applying TRP to FCN for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(FCNConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, DeepLabV3):
+        print("✅ Applying TRP to DeepLabV3 for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(DeepLabV3Config.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, transformers.BertForSequenceClassification):
+        print("✅ Applying TRP to Bert for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.RobertaForSequenceClassification):
+        print("✅ Applying TRP to Roberta for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(RobertaForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.Wav2Vec2ForCTC):
+        print("✅ Applying TRP to Wav2Vec2 for Speech Recognition...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForCTCConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.MBartForConditionalGeneration):
+        print("✅ Applying TRP to MBart for Translation...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(MBartForConditionalGenerationConfig.gen_forward(lambdas, False), model)
+    else:
+        torch._assert(
+            isinstance(model, transformers.Wav2Vec2ForSequenceClassification), 
+            "The model should be an object of [`Wav2Vec2ForSequenceClassification`].")
+
+    return model
\ No newline at end of file
diff --git a/hpo-examples/image-classification/utils.py b/hpo-examples/image-classification/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c31f3928e8641acabf725be129895f0533ecd29e
--- /dev/null
+++ b/hpo-examples/image-classification/utils.py
@@ -0,0 +1,465 @@
+import copy
+import datetime
+import errno
+import hashlib
+import os
+import time
+from collections import defaultdict, deque, OrderedDict
+from typing import List, Optional, Tuple
+
+import torch
+import torch.distributed as dist
+
+
+class SmoothedValue:
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        t = reduce_across_processes([self.count, self.total])
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value
+        )
+
+
+class MetricLogger:
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{attr}'")
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append(f"{name}: {str(meter)}")
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None):
+        i = 0
+        if not header:
+            header = ""
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt="{avg:.4f}")
+        data_time = SmoothedValue(fmt="{avg:.4f}")
+        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
+        if torch.cuda.is_available():
+            log_msg = self.delimiter.join(
+                [
+                    header,
+                    "[{0" + space_fmt + "}/{1}]",
+                    "eta: {eta}",
+                    "{meters}",
+                    "time: {time}",
+                    "data: {data}",
+                    "max mem: {memory:.0f}",
+                ]
+            )
+        else:
+            log_msg = self.delimiter.join(
+                [header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}"]
+            )
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB,
+                        )
+                    )
+                else:
+                    print(
+                        log_msg.format(
+                            i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time)
+                        )
+                    )
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print(f"{header} Total time: {total_time_str}")
+
+
+class ExponentialMovingAverage(torch.optim.swa_utils.AveragedModel):
+    """Maintains moving averages of model parameters using an exponential decay.
+    ``ema_avg = decay * avg_model_param + (1 - decay) * model_param``
+    `torch.optim.swa_utils.AveragedModel <https://pytorch.org/docs/stable/optim.html#custom-averaging-strategies>`_
+    is used to compute the EMA.
+    """
+
+    def __init__(self, model, decay, device="cpu"):
+        def ema_avg(avg_model_param, model_param, num_averaged):
+            return decay * avg_model_param + (1 - decay) * model_param
+
+        super().__init__(model, device, ema_avg, use_buffers=True)
+
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.inference_mode():
+        maxk = max(topk)
+        batch_size = target.size(0)
+        if target.ndim == 2:
+            target = target.max(dim=1)[1]
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target[None])
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].flatten().sum(dtype=torch.float32)
+            res.append(correct_k * (100.0 / batch_size))
+        return res
+
+
+def mkdir(path):
+    try:
+        os.makedirs(path)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)
+
+
+def init_distributed_mode(args):
+    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ["WORLD_SIZE"])
+        args.gpu = int(os.environ["LOCAL_RANK"])
+    elif "SLURM_PROCID" in os.environ:
+        args.rank = int(os.environ["SLURM_PROCID"])
+        args.gpu = args.rank % torch.cuda.device_count()
+    elif hasattr(args, "rank"):
+        pass
+    else:
+        print("Not using distributed mode")
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = "nccl"
+    print(f"| distributed init (rank {args.rank}): {args.dist_url}", flush=True)
+    torch.distributed.init_process_group(
+        backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
+    )
+    torch.distributed.barrier()
+    setup_for_distributed(args.rank == 0)
+
+
+def average_checkpoints(inputs):
+    """Loads checkpoints from inputs and returns a model with averaged weights. Original implementation taken from:
+    https://github.com/pytorch/fairseq/blob/a48f235636557b8d3bc4922a6fa90f3a0fa57955/scripts/average_checkpoints.py#L16
+
+    Args:
+      inputs (List[str]): An iterable of string paths of checkpoints to load from.
+    Returns:
+      A dict of string keys mapping to various values. The 'model' key
+      from the returned dict should correspond to an OrderedDict mapping
+      string parameter names to torch Tensors.
+    """
+    params_dict = OrderedDict()
+    params_keys = None
+    new_state = None
+    num_models = len(inputs)
+    for fpath in inputs:
+        with open(fpath, "rb") as f:
+            state = torch.load(
+                f,
+                map_location=(lambda s, _: torch.serialization.default_restore_location(s, "cpu")),
+            )
+        # Copies over the settings from the first checkpoint
+        if new_state is None:
+            new_state = state
+        model_params = state["model"]
+        model_params_keys = list(model_params.keys())
+        if params_keys is None:
+            params_keys = model_params_keys
+        elif params_keys != model_params_keys:
+            raise KeyError(
+                f"For checkpoint {f}, expected list of params: {params_keys}, but found: {model_params_keys}"
+            )
+        for k in params_keys:
+            p = model_params[k]
+            if isinstance(p, torch.HalfTensor):
+                p = p.float()
+            if k not in params_dict:
+                params_dict[k] = p.clone()
+                # NOTE: clone() is needed in case of p is a shared parameter
+            else:
+                params_dict[k] += p
+    averaged_params = OrderedDict()
+    for k, v in params_dict.items():
+        averaged_params[k] = v
+        if averaged_params[k].is_floating_point():
+            averaged_params[k].div_(num_models)
+        else:
+            averaged_params[k] //= num_models
+    new_state["model"] = averaged_params
+    return new_state
+
+
+def store_model_weights(model, checkpoint_path, checkpoint_key="model", strict=True):
+    """
+    This method can be used to prepare weights files for new models. It receives as
+    input a model architecture and a checkpoint from the training script and produces
+    a file with the weights ready for release.
+
+    Examples:
+        from torchvision import models as M
+
+        # Classification
+        model = M.mobilenet_v3_large(weights=None)
+        print(store_model_weights(model, './class.pth'))
+
+        # Quantized Classification
+        model = M.quantization.mobilenet_v3_large(weights=None, quantize=False)
+        model.fuse_model(is_qat=True)
+        model.qconfig = torch.ao.quantization.get_default_qat_qconfig('qnnpack')
+        _ = torch.ao.quantization.prepare_qat(model, inplace=True)
+        print(store_model_weights(model, './qat.pth'))
+
+        # Object Detection
+        model = M.detection.fasterrcnn_mobilenet_v3_large_fpn(weights=None, weights_backbone=None)
+        print(store_model_weights(model, './obj.pth'))
+
+        # Segmentation
+        model = M.segmentation.deeplabv3_mobilenet_v3_large(weights=None, weights_backbone=None, aux_loss=True)
+        print(store_model_weights(model, './segm.pth', strict=False))
+
+    Args:
+        model (pytorch.nn.Module): The model on which the weights will be loaded for validation purposes.
+        checkpoint_path (str): The path of the checkpoint we will load.
+        checkpoint_key (str, optional): The key of the checkpoint where the model weights are stored.
+            Default: "model".
+        strict (bool): whether to strictly enforce that the keys
+            in :attr:`state_dict` match the keys returned by this module's
+            :meth:`~torch.nn.Module.state_dict` function. Default: ``True``
+
+    Returns:
+        output_path (str): The location where the weights are saved.
+    """
+    # Store the new model next to the checkpoint_path
+    checkpoint_path = os.path.abspath(checkpoint_path)
+    output_dir = os.path.dirname(checkpoint_path)
+
+    # Deep copy to avoid side-effects on the model object.
+    model = copy.deepcopy(model)
+    checkpoint = torch.load(checkpoint_path, map_location="cpu")
+
+    # Load the weights to the model to validate that everything works
+    # and remove unnecessary weights (such as auxiliaries, etc)
+    if checkpoint_key == "model_ema":
+        del checkpoint[checkpoint_key]["n_averaged"]
+        torch.nn.modules.utils.consume_prefix_in_state_dict_if_present(checkpoint[checkpoint_key], "module.")
+    model.load_state_dict(checkpoint[checkpoint_key], strict=strict)
+
+    tmp_path = os.path.join(output_dir, str(model.__hash__()))
+    torch.save(model.state_dict(), tmp_path)
+
+    sha256_hash = hashlib.sha256()
+    with open(tmp_path, "rb") as f:
+        # Read and update hash string value in blocks of 4K
+        for byte_block in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(byte_block)
+        hh = sha256_hash.hexdigest()
+
+    output_path = os.path.join(output_dir, "weights-" + str(hh[:8]) + ".pth")
+    os.replace(tmp_path, output_path)
+
+    return output_path
+
+
+def reduce_across_processes(val):
+    if not is_dist_avail_and_initialized():
+        # nothing to sync, but we still convert to tensor for consistency with the distributed case.
+        return torch.tensor(val)
+
+    t = torch.tensor(val, device="cuda")
+    dist.barrier()
+    dist.all_reduce(t)
+    return t
+
+
+def set_weight_decay(
+    model: torch.nn.Module,
+    weight_decay: float,
+    norm_weight_decay: Optional[float] = None,
+    norm_classes: Optional[List[type]] = None,
+    custom_keys_weight_decay: Optional[List[Tuple[str, float]]] = None,
+):
+    if not norm_classes:
+        norm_classes = [
+            torch.nn.modules.batchnorm._BatchNorm,
+            torch.nn.LayerNorm,
+            torch.nn.GroupNorm,
+            torch.nn.modules.instancenorm._InstanceNorm,
+            torch.nn.LocalResponseNorm,
+        ]
+    norm_classes = tuple(norm_classes)
+
+    params = {
+        "other": [],
+        "norm": [],
+    }
+    params_weight_decay = {
+        "other": weight_decay,
+        "norm": norm_weight_decay,
+    }
+    custom_keys = []
+    if custom_keys_weight_decay is not None:
+        for key, weight_decay in custom_keys_weight_decay:
+            params[key] = []
+            params_weight_decay[key] = weight_decay
+            custom_keys.append(key)
+
+    def _add_params(module, prefix=""):
+        for name, p in module.named_parameters(recurse=False):
+            if not p.requires_grad:
+                continue
+            is_custom_key = False
+            for key in custom_keys:
+                target_name = f"{prefix}.{name}" if prefix != "" and "." in key else name
+                if key == target_name:
+                    params[key].append(p)
+                    is_custom_key = True
+                    break
+            if not is_custom_key:
+                if norm_weight_decay is not None and isinstance(module, norm_classes):
+                    params["norm"].append(p)
+                else:
+                    params["other"].append(p)
+
+        for child_name, child_module in module.named_children():
+            child_prefix = f"{prefix}.{child_name}" if prefix != "" else child_name
+            _add_params(child_module, prefix=child_prefix)
+
+    _add_params(model)
+
+    param_groups = []
+    for key in params:
+        if len(params[key]) > 0:
+            param_groups.append({"params": params[key], "weight_decay": params_weight_decay[key]})
+    return param_groups
diff --git a/hpo-examples/image-classification/vit_b_16/model_4.pth b/hpo-examples/image-classification/vit_b_16/model_4.pth
new file mode 100644
index 0000000000000000000000000000000000000000..4728e7f9bea5ee4752b6f6339f337894bcfe75a3
--- /dev/null
+++ b/hpo-examples/image-classification/vit_b_16/model_4.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29f1bd991a3f27f7982e8700f31332cb94c4783b83e240fc71f1eca03d4eb468
+size 1053172110
diff --git a/hpo-examples/question-answering/qa/README.md b/hpo-examples/question-answering/qa/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..dedcd9fe9f3d0f283a80a2ab1e4a07eab16cdf89
--- /dev/null
+++ b/hpo-examples/question-answering/qa/README.md
@@ -0,0 +1,55 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- squad
+model-index:
+- name: baseline
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# baseline
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the squad dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 12
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 2.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/question-answering/qa/all_results.json b/hpo-examples/question-answering/qa/all_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..28c0213454bab27e66d3de5aa3a44af56f0d753a
--- /dev/null
+++ b/hpo-examples/question-answering/qa/all_results.json
@@ -0,0 +1,15 @@
+{
+    "epoch": 2.0,
+    "eval_exact_match": 81.49479659413434,
+    "eval_f1": 88.62945564424126,
+    "eval_runtime": 61.0301,
+    "eval_samples": 10784,
+    "eval_samples_per_second": 176.7,
+    "eval_steps_per_second": 22.087,
+    "total_flos": 3.541929151120589e+16,
+    "train_loss": 1.148573803161563,
+    "train_runtime": 3245.3985,
+    "train_samples": 88524,
+    "train_samples_per_second": 54.554,
+    "train_steps_per_second": 4.546
+}
\ No newline at end of file
diff --git a/hpo-examples/question-answering/qa/config.json b/hpo-examples/question-answering/qa/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..303c0a5850b2ba8b060fd58136e5905181713459
--- /dev/null
+++ b/hpo-examples/question-answering/qa/config.json
@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "google-bert/bert-base-uncased",
+  "architectures": [
+    "BertForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}
diff --git a/hpo-examples/question-answering/qa/eval_nbest_predictions.json b/hpo-examples/question-answering/qa/eval_nbest_predictions.json
new file mode 100644
index 0000000000000000000000000000000000000000..e8215ee10b8042b3340fd75b27057ead87462ee6
--- /dev/null
+++ b/hpo-examples/question-answering/qa/eval_nbest_predictions.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b8d44953cbe0ce20d1d1b62b72e7adba18bf1dc81d055492e22bfa21ff46657
+size 49596120
diff --git a/hpo-examples/question-answering/qa/eval_predictions.json b/hpo-examples/question-answering/qa/eval_predictions.json
new file mode 100644
index 0000000000000000000000000000000000000000..f86f4a188db999c7cec062ecce11744e77d42889
--- /dev/null
+++ b/hpo-examples/question-answering/qa/eval_predictions.json
@@ -0,0 +1,10572 @@
+{
+    "56be4db0acb8001400a502ec": "Denver Broncos",
+    "56be4db0acb8001400a502ed": "Carolina Panthers",
+    "56be4db0acb8001400a502ee": "Levi's Stadium in the San Francisco Bay Area at Santa Clara, California",
+    "56be4db0acb8001400a502ef": "Denver Broncos",
+    "56be4db0acb8001400a502f0": "gold",
+    "56be8e613aeaaa14008c90d1": "golden anniversary",
+    "56be8e613aeaaa14008c90d2": "February 7, 2016",
+    "56be8e613aeaaa14008c90d3": "American Football Conference",
+    "56bea9923aeaaa14008c91b9": "golden anniversary",
+    "56bea9923aeaaa14008c91ba": "American Football Conference",
+    "56bea9923aeaaa14008c91bb": "February 7, 2016",
+    "56beace93aeaaa14008c91df": "Denver Broncos",
+    "56beace93aeaaa14008c91e0": "Levi's Stadium",
+    "56beace93aeaaa14008c91e1": "Santa Clara",
+    "56beace93aeaaa14008c91e2": "Super Bowl L",
+    "56beace93aeaaa14008c91e3": "2015",
+    "56bf10f43aeaaa14008c94fd": "2016",
+    "56bf10f43aeaaa14008c94fe": "Santa Clara",
+    "56bf10f43aeaaa14008c94ff": "Levi's Stadium",
+    "56bf10f43aeaaa14008c9500": "50th",
+    "56bf10f43aeaaa14008c9501": "February 7, 2016",
+    "56d20362e7d4791d009025e8": "2016",
+    "56d20362e7d4791d009025e9": "Denver Broncos",
+    "56d20362e7d4791d009025ea": "Carolina Panthers",
+    "56d20362e7d4791d009025eb": "Denver Broncos",
+    "56d600e31c85041400946eae": "2015",
+    "56d600e31c85041400946eb0": "Denver Broncos",
+    "56d600e31c85041400946eb1": "Levi's Stadium in the San Francisco Bay Area at Santa Clara, California",
+    "56d9895ddc89441400fdb50e": "Super Bowl 50",
+    "56d9895ddc89441400fdb510": "Denver Broncos",
+    "56be4e1facb8001400a502f6": "Cam Newton",
+    "56be4e1facb8001400a502f9": "eight",
+    "56be4e1facb8001400a502fa": "1995",
+    "56beaa4a3aeaaa14008c91c2": "Arizona Cardinals",
+    "56beaa4a3aeaaa14008c91c3": "New England Patriots",
+    "56bead5a3aeaaa14008c91e9": "Arizona Cardinals",
+    "56bead5a3aeaaa14008c91ea": "Arizona Cardinals",
+    "56bead5a3aeaaa14008c91eb": "New England Patriots",
+    "56bead5a3aeaaa14008c91ec": "four",
+    "56bead5a3aeaaa14008c91ed": "Cam Newton",
+    "56bf159b3aeaaa14008c9507": "15\u20131",
+    "56bf159b3aeaaa14008c9508": "Cam Newton",
+    "56bf159b3aeaaa14008c9509": "12\u20134",
+    "56bf159b3aeaaa14008c950a": "four",
+    "56bf159b3aeaaa14008c950b": "New England Patriots",
+    "56d2045de7d4791d009025f3": "Cam Newton",
+    "56d2045de7d4791d009025f4": "Arizona Cardinals",
+    "56d2045de7d4791d009025f5": "eight",
+    "56d2045de7d4791d009025f6": "Arizona Cardinals",
+    "56d6017d1c85041400946ebe": "Cam Newton",
+    "56d6017d1c85041400946ec1": "Arizona Cardinals",
+    "56d6017d1c85041400946ec2": "Arizona Cardinals",
+    "56d98a59dc89441400fdb52a": "Cam Newton",
+    "56d98a59dc89441400fdb52b": "Arizona Cardinals",
+    "56d98a59dc89441400fdb52e": "1995",
+    "56be4eafacb8001400a50302": "Von Miller",
+    "56be4eafacb8001400a50303": "two",
+    "56be4eafacb8001400a50304": "The Broncos",
+    "56beab833aeaaa14008c91d2": "Von Miller",
+    "56beab833aeaaa14008c91d3": "five",
+    "56beab833aeaaa14008c91d4": "Newton",
+    "56beae423aeaaa14008c91f4": "seven",
+    "56beae423aeaaa14008c91f5": "Von Miller",
+    "56beae423aeaaa14008c91f6": "three",
+    "56beae423aeaaa14008c91f7": "two",
+    "56bf17653aeaaa14008c9511": "Von Miller",
+    "56bf17653aeaaa14008c9513": "linebacker",
+    "56bf17653aeaaa14008c9514": "five",
+    "56bf17653aeaaa14008c9515": "two",
+    "56d204ade7d4791d00902603": "Von Miller",
+    "56d204ade7d4791d00902604": "five",
+    "56d601e41c85041400946ece": "seven",
+    "56d601e41c85041400946ecf": "three",
+    "56d601e41c85041400946ed0": "fumble",
+    "56d601e41c85041400946ed1": "Von Miller",
+    "56d601e41c85041400946ed2": "Denver linebacker",
+    "56d98b33dc89441400fdb53b": "seven",
+    "56d98b33dc89441400fdb53c": "three",
+    "56d98b33dc89441400fdb53d": "Von Miller",
+    "56d98b33dc89441400fdb53e": "five",
+    "56be5333acb8001400a5030a": "CBS",
+    "56be5333acb8001400a5030b": "$5 million",
+    "56be5333acb8001400a5030c": "Coldplay",
+    "56be5333acb8001400a5030d": "Beyonc\u00e9 and Bruno Mars",
+    "56be5333acb8001400a5030e": "Super Bowl XLVIII",
+    "56beaf5e3aeaaa14008c91fd": "CBS",
+    "56beaf5e3aeaaa14008c91fe": "$5 million",
+    "56beaf5e3aeaaa14008c91ff": "Beyonc\u00e9 and Bruno Mars",
+    "56beaf5e3aeaaa14008c9200": "Beyonc\u00e9 and Bruno Mars",
+    "56beaf5e3aeaaa14008c9201": "Beyonc\u00e9 and Bruno Mars",
+    "56bf1ae93aeaaa14008c951b": "CBS",
+    "56bf1ae93aeaaa14008c951c": "$5 million",
+    "56bf1ae93aeaaa14008c951e": "Beyonc\u00e9 and Bruno Mars",
+    "56bf1ae93aeaaa14008c951f": "third",
+    "56d2051ce7d4791d00902608": "CBS",
+    "56d2051ce7d4791d00902609": "$5 million",
+    "56d2051ce7d4791d0090260a": "Coldplay",
+    "56d2051ce7d4791d0090260b": "Beyonc\u00e9 and Bruno Mars",
+    "56d602631c85041400946ed8": "CBS",
+    "56d602631c85041400946eda": "Coldplay",
+    "56d602631c85041400946edb": "Beyonc\u00e9 and Bruno Mars",
+    "56d602631c85041400946edc": "Super Bowl XLVIII",
+    "56d98c53dc89441400fdb544": "$5 million",
+    "56d98c53dc89441400fdb545": "Coldplay",
+    "56d98c53dc89441400fdb546": "Beyonc\u00e9 and Bruno Mars",
+    "56d98c53dc89441400fdb548": "Beyonc\u00e9 and Bruno Mars",
+    "56be53b8acb8001400a50314": "Roger Goodell",
+    "56be53b8acb8001400a50315": "50th",
+    "56be53b8acb8001400a50316": "2012",
+    "56beafca3aeaaa14008c9207": "Roger Goodell",
+    "56beafca3aeaaa14008c9208": "early 2012",
+    "56bf42f53aeaaa14008c95a3": "Roger Goodell",
+    "56d2053ae7d4791d00902610": "Roger Goodell",
+    "56d6edd00d65d21400198250": "Roger Goodell",
+    "56d6edd00d65d21400198251": "spectacular",
+    "56d98d0adc89441400fdb54e": "spectacular",
+    "56d98d0adc89441400fdb54f": "2012",
+    "56be5438acb8001400a5031a": "Mercedes-Benz Superdome",
+    "56be5438acb8001400a5031b": "Sun Life Stadium",
+    "56be5438acb8001400a5031c": "Levi's Stadium",
+    "56beb03c3aeaaa14008c920b": "Sun Life Stadium",
+    "56beb03c3aeaaa14008c920d": "Levi's Stadium",
+    "56bf3c633aeaaa14008c9580": "Levi's Stadium",
+    "56bf3c633aeaaa14008c9581": "Mercedes-Benz Superdome",
+    "56bf3c633aeaaa14008c9582": "Sun Life Stadium",
+    "56d20564e7d4791d00902612": "New Orleans' Mercedes-Benz Superdome, Miami's Sun Life Stadium, and the San Francisco Bay Area's Levi's Stadium",
+    "56d6ee6e0d65d21400198254": "three",
+    "56d6ee6e0d65d21400198255": "New Orleans",
+    "56d6ee6e0d65d21400198256": "Sun Life Stadium",
+    "56d6ee6e0d65d21400198257": "New Orleans",
+    "56d6ee6e0d65d21400198258": "Levi's Stadium",
+    "56d98db6dc89441400fdb552": "Sun Life Stadium",
+    "56d98db6dc89441400fdb553": "Mercedes-Benz Superdome",
+    "56d98db6dc89441400fdb554": "Levi's Stadium",
+    "56be54bdacb8001400a50322": "October 16, 2012",
+    "56be54bdacb8001400a50323": "10",
+    "56be54bdacb8001400a50324": "Super Bowl XLIV",
+    "56be54bdacb8001400a50325": "2010",
+    "56be54bdacb8001400a50326": "1985",
+    "56beb0f43aeaaa14008c921b": "Sun Life Stadium",
+    "56beb0f43aeaaa14008c921c": "October 16, 2012",
+    "56beb0f43aeaaa14008c921d": "Stanford Stadium",
+    "56beb0f43aeaaa14008c921e": "May 3, 2013",
+    "56beb0f43aeaaa14008c921f": "1985",
+    "56bf21b43aeaaa14008c9525": "10",
+    "56bf21b43aeaaa14008c9526": "Super Bowl XIX",
+    "56bf21b43aeaaa14008c9528": "two",
+    "56bf21b43aeaaa14008c9529": "The league",
+    "56d6ef6a0d65d21400198260": "1985",
+    "56d6ef6a0d65d21400198262": "New Orleans",
+    "56d98f0ddc89441400fdb558": "October 16, 2012",
+    "56d98f0ddc89441400fdb559": "10",
+    "56d98f0ddc89441400fdb55a": "New Orleans",
+    "56d98f0ddc89441400fdb55b": "1985",
+    "56d98f0ddc89441400fdb55c": "Florida legislature",
+    "56be5523acb8001400a5032c": "May 21, 2013",
+    "56be5523acb8001400a5032d": "NFL owners",
+    "56be5523acb8001400a5032e": "2014",
+    "56be5523acb8001400a5032f": "$1.2 billion",
+    "56be5523acb8001400a50330": "San Diego",
+    "56beb2153aeaaa14008c9225": "Boston",
+    "56beb2153aeaaa14008c9226": "May 21, 2013",
+    "56beb2153aeaaa14008c9227": "$1.2 billion",
+    "56beb2153aeaaa14008c9228": "Super Bowl XXXVII",
+    "56beb2153aeaaa14008c9229": "San Diego",
+    "56bf23363aeaaa14008c952f": "2013",
+    "56bf23363aeaaa14008c9530": "2014",
+    "56bf23363aeaaa14008c9531": "$1.2 billion",
+    "56bf23363aeaaa14008c9532": "1985",
+    "56bf23363aeaaa14008c9533": "Super Bowl XXXVII",
+    "56d6f0770d65d21400198268": "May 21, 2013",
+    "56d6f0770d65d21400198269": "2014",
+    "56d6f0770d65d2140019826a": "2003",
+    "56d6f0770d65d2140019826c": "Boston",
+    "56d98fbfdc89441400fdb562": "May 21, 2013",
+    "56d98fbfdc89441400fdb563": "2014",
+    "56d98fbfdc89441400fdb564": "$1.2 billion",
+    "56d98fbfdc89441400fdb565": "2003",
+    "56be572b3aeaaa14008c9052": "John Fox",
+    "56beb2a03aeaaa14008c922f": "ten",
+    "56beb2a03aeaaa14008c9230": "six",
+    "56beb2a03aeaaa14008c9231": "Carolina Panthers",
+    "56beb2a03aeaaa14008c9232": "Super Bowl XXXVIII",
+    "56beb2a03aeaaa14008c9233": "John Fox",
+    "56bf28c73aeaaa14008c9539": "eight",
+    "56bf28c73aeaaa14008c953a": "ten",
+    "56bf28c73aeaaa14008c953c": "Super Bowl XXXVIII",
+    "56bf28c73aeaaa14008c953d": "six",
+    "56d6f1190d65d21400198272": "number one",
+    "56d6f1190d65d21400198273": "the Denver Broncos became one of four teams to have made eight appearances in the Super Bowl",
+    "56d6f1190d65d21400198274": "Super Bowl XXXVIII",
+    "56d6f1190d65d21400198275": "Super Bowl XXXVIII",
+    "56d6f1190d65d21400198276": "six",
+    "56d99179dc89441400fdb56c": "one",
+    "56d99179dc89441400fdb56d": "four",
+    "56d99179dc89441400fdb570": "John Fox",
+    "56be59683aeaaa14008c9058": "DeAngelo Williams",
+    "56be59683aeaaa14008c9059": "Kelvin Benjamin",
+    "56be59683aeaaa14008c905a": "seventh",
+    "56beb3083aeaaa14008c923d": "1978",
+    "56beb3083aeaaa14008c923e": "Carolina Panthers",
+    "56beb3083aeaaa14008c923f": "Ten",
+    "56beb3083aeaaa14008c9240": "eight",
+    "56beb3083aeaaa14008c9241": "Carolina Panthers",
+    "56bf2afe3aeaaa14008c9543": "1978",
+    "56bf2afe3aeaaa14008c9544": "2009",
+    "56bf2afe3aeaaa14008c9545": "2011",
+    "56bf2afe3aeaaa14008c9547": "torn ACL",
+    "56d6f2000d65d2140019827c": "Kelvin Benjamin",
+    "56d6f2000d65d2140019827d": "DeAngelo Williams",
+    "56d6f2000d65d2140019827e": "1978",
+    "56d6f2000d65d2140019827f": "Ten",
+    "56d9943fdc89441400fdb576": "Carolina Panthers",
+    "56d9943fdc89441400fdb577": "1978",
+    "56d9943fdc89441400fdb578": "Carolina Panthers",
+    "56d9943fdc89441400fdb57a": "Ten",
+    "56beb3a03aeaaa14008c9247": "six",
+    "56beb3a03aeaaa14008c9248": "45",
+    "56beb3a03aeaaa14008c9249": "10",
+    "56beb3a03aeaaa14008c924a": "27",
+    "56beb3a03aeaaa14008c924b": "Greg Olsen",
+    "56bf6b303aeaaa14008c960b": "45",
+    "56bf6b303aeaaa14008c960c": "99.4",
+    "56bf6b303aeaaa14008c960d": "77",
+    "56bf6b303aeaaa14008c960e": "receivers",
+    "56bf6b303aeaaa14008c960f": "Jonathan Stewart",
+    "56d6f2960d65d21400198286": "six",
+    "56d6f2960d65d21400198287": "Cam Newton",
+    "56d6f2960d65d21400198288": "3,837",
+    "56d6f2960d65d21400198289": "45",
+    "56d6f2960d65d2140019828a": "six",
+    "56d997cddc89441400fdb586": "500",
+    "56d997cddc89441400fdb587": "3,837",
+    "56d997cddc89441400fdb588": "45",
+    "56d997cddc89441400fdb589": "99.4",
+    "56d997cddc89441400fdb58a": "39",
+    "56beb4343aeaaa14008c925b": "308",
+    "56beb4343aeaaa14008c925c": "136",
+    "56beb4343aeaaa14008c925d": "118",
+    "56beb4343aeaaa14008c925e": "88",
+    "56beb4343aeaaa14008c925f": "Kawann Short",
+    "56d6f3500d65d21400198290": "24",
+    "56d6f3500d65d21400198291": "Kawann Short",
+    "56d6f3500d65d21400198292": "four",
+    "56d6f3500d65d21400198293": "four",
+    "56d6f3500d65d21400198294": "Josh Norman",
+    "56d9992fdc89441400fdb59c": "24",
+    "56d9992fdc89441400fdb59e": "Kony Ealy",
+    "56d9992fdc89441400fdb59f": "Jared Allen",
+    "56d9992fdc89441400fdb5a0": "four",
+    "56beb4e43aeaaa14008c9265": "Gary Kubiak",
+    "56beb4e43aeaaa14008c9266": "Brock Osweiler",
+    "56beb4e43aeaaa14008c9267": "Indianapolis Colts",
+    "56beb4e43aeaaa14008c9268": "Brock Osweiler",
+    "56beb4e43aeaaa14008c9269": "Wade Phillips",
+    "56bf301c3aeaaa14008c954d": "four",
+    "56bf301c3aeaaa14008c954e": "Gary Kubiak",
+    "56bf301c3aeaaa14008c954f": "Indianapolis Colts",
+    "56bf301c3aeaaa14008c9550": "39",
+    "56bf301c3aeaaa14008c9551": "partial tear of the plantar fasciitis in his left foot",
+    "56d6f4030d65d2140019829a": "Gary Kubiak",
+    "56d6f4030d65d2140019829b": "Peyton Manning",
+    "56d6f4030d65d2140019829c": "plantar fasciitis injury in his heel",
+    "56d6f4030d65d2140019829d": "39",
+    "56d6f4030d65d2140019829e": "13\u20137",
+    "56d99b7bdc89441400fdb5c8": "John Fox",
+    "56d99b7bdc89441400fdb5c9": "Peyton Manning's shotgun passing skills, but struggled with numerous changes and injuries to the offensive line, as well as Manning",
+    "56d99b7bdc89441400fdb5ca": "Gary Kubiak",
+    "56d99b7bdc89441400fdb5cb": "left",
+    "56d99b7bdc89441400fdb5cc": "Wade Phillips",
+    "56beb57b3aeaaa14008c9279": "67.9",
+    "56beb57b3aeaaa14008c927a": "2,249",
+    "56beb57b3aeaaa14008c927b": "Demaryius Thomas",
+    "56beb57b3aeaaa14008c927c": "C. J. Anderson",
+    "56beb57b3aeaaa14008c927d": "10",
+    "56bf38383aeaaa14008c956b": "67.9",
+    "56bf38383aeaaa14008c956c": "2,249",
+    "56bf38383aeaaa14008c956d": "six",
+    "56bf38383aeaaa14008c956e": "Demaryius Thomas",
+    "56bf38383aeaaa14008c956f": "receiver",
+    "56d6fe0b0d65d214001982a4": "67.9",
+    "56d6fe0b0d65d214001982a5": "17",
+    "56d6fe0b0d65d214001982a6": "Demaryius Thomas",
+    "56d6fe0b0d65d214001982a7": "five",
+    "56d99c44dc89441400fdb5d6": "67.9",
+    "56d99c44dc89441400fdb5d7": "17",
+    "56d99c44dc89441400fdb5d8": "Emmanuel Sanders",
+    "56d99c44dc89441400fdb5d9": "C. J. Anderson",
+    "56d99c44dc89441400fdb5da": "4.7",
+    "56beb6533aeaaa14008c928d": "4,530",
+    "56beb6533aeaaa14008c928e": "5\u00bd",
+    "56beb6533aeaaa14008c9290": "Brandon Marshall",
+    "56beb6533aeaaa14008c9291": "three interceptions) and Chris Harris, Jr. (two interceptions) were the other two",
+    "56bf3e803aeaaa14008c9588": "Linebacker",
+    "56bf3e803aeaaa14008c9589": "Linebacker",
+    "56bf3e803aeaaa14008c958b": "Defensive ends",
+    "56d6fea90d65d214001982ae": "296",
+    "56d6fea90d65d214001982af": "Brandon Marshall",
+    "56d6fea90d65d214001982b0": "Brandon Marshall",
+    "56d6fea90d65d214001982b2": "three",
+    "56d99da8dc89441400fdb5fd": "Von Miller",
+    "56d99da8dc89441400fdb5ff": "Brandon Marshall",
+    "56d99da8dc89441400fdb600": "Derek Wolfe and Malik Jackson",
+    "56beb6f23aeaaa14008c92a1": "Seattle Seahawks",
+    "56beb6f23aeaaa14008c92a2": "Arizona Cardinals",
+    "56beb6f23aeaaa14008c92a3": "487",
+    "56beb6f23aeaaa14008c92a4": "49\u201315",
+    "56beb6f23aeaaa14008c92a5": "31\u20130 halftime lead and then holding off a furious second half comeback attempt to win 31\u201324",
+    "56bf34293aeaaa14008c9559": "Arizona Cardinals",
+    "56bf34293aeaaa14008c955a": "49\u201315",
+    "56bf34293aeaaa14008c955b": "49\u201315, racking up 487 yards",
+    "56d701280d65d214001982b8": "Seattle Seahawks",
+    "56d701280d65d214001982ba": "Seattle Seahawks",
+    "56d701280d65d214001982bc": "seven",
+    "56d99e78dc89441400fdb616": "Seattle Seahawks",
+    "56d99e78dc89441400fdb618": "49\u201315",
+    "56d99e78dc89441400fdb619": "Arizona Cardinals",
+    "56d99e78dc89441400fdb61a": "487",
+    "56beb7953aeaaa14008c92ab": "Pittsburgh Steelers",
+    "56beb7953aeaaa14008c92ac": "11",
+    "56beb7953aeaaa14008c92ad": "New England Patriots",
+    "56beb7953aeaaa14008c92ae": "20\u201318",
+    "56beb7953aeaaa14008c92af": "17 seconds",
+    "56bf36b93aeaaa14008c9561": "New England Patriots",
+    "56bf36b93aeaaa14008c9562": "11 points",
+    "56bf36b93aeaaa14008c9563": "New England Patriots",
+    "56bf36b93aeaaa14008c9564": "17",
+    "56bf36b93aeaaa14008c9565": "Manning",
+    "56d7018a0d65d214001982c2": "Pittsburgh Steelers",
+    "56d7018a0d65d214001982c3": "11",
+    "56d7018a0d65d214001982c5": "New England Patriots",
+    "56d99f99dc89441400fdb628": "Pittsburgh Steelers",
+    "56d99f99dc89441400fdb629": "New England Patriots",
+    "56d99f99dc89441400fdb62c": "17",
+    "56beb7fd3aeaaa14008c92b5": "Thomas Davis",
+    "56beb7fd3aeaaa14008c92b6": "broken arm",
+    "56beb7fd3aeaaa14008c92b7": "three",
+    "56beb7fd3aeaaa14008c92b8": "11",
+    "56bf3a223aeaaa14008c9575": "ACL tears in his career, went down with a broken arm",
+    "56bf3a223aeaaa14008c9576": "arm",
+    "56bf3a223aeaaa14008c9577": "11",
+    "56bf3a223aeaaa14008c9579": "Super Bowl",
+    "56d703d10d65d214001982d6": "three",
+    "56d703d10d65d214001982d7": "broken arm",
+    "56d703d10d65d214001982d8": "11",
+    "56d9a026dc89441400fdb632": "Thomas Davis",
+    "56beb86b3aeaaa14008c92bd": "39",
+    "56beb86b3aeaaa14008c92be": "John Elway",
+    "56beb86b3aeaaa14008c92bf": "38",
+    "56beb86b3aeaaa14008c92c0": "Executive Vice President of Football Operations and General Manager",
+    "56beb86b3aeaaa14008c92c1": "Broncos",
+    "56bf3fd53aeaaa14008c9591": "Broncos",
+    "56bf3fd53aeaaa14008c9592": "John Elway",
+    "56bf3fd53aeaaa14008c9593": "38",
+    "56bf3fd53aeaaa14008c9594": "John Elway",
+    "56bf3fd53aeaaa14008c9595": "two",
+    "56d704430d65d214001982de": "two",
+    "56d704430d65d214001982e0": "Peyton Manning",
+    "56d704430d65d214001982e1": "John Elway",
+    "56d704430d65d214001982e2": "Super Bowl XXXIII",
+    "56d9a0eadc89441400fdb63e": "Peyton Manning",
+    "56d9a0eadc89441400fdb63f": "39",
+    "56d9a0eadc89441400fdb640": "John Elway",
+    "56beb90c3aeaaa14008c92c7": "1998",
+    "56beb90c3aeaaa14008c92c8": "2011",
+    "56beb90c3aeaaa14008c92c9": "26",
+    "56beb90c3aeaaa14008c92ca": "26",
+    "56beb90c3aeaaa14008c92cb": "Newton",
+    "56bf41013aeaaa14008c959b": "Manning",
+    "56bf41013aeaaa14008c959c": "Newton",
+    "56bf41013aeaaa14008c959d": "26",
+    "56bf41013aeaaa14008c959e": "quarterback",
+    "56d7096b0d65d214001982fc": "1998",
+    "56d7096b0d65d214001982fd": "2011",
+    "56d7096b0d65d214001982fe": "Newton",
+    "56d9a199dc89441400fdb647": "2011",
+    "56d9a199dc89441400fdb648": "26",
+    "56d9a199dc89441400fdb649": "26",
+    "56beb97c3aeaaa14008c92db": "Super Bowl XX",
+    "56beb97c3aeaaa14008c92dc": "Chicago Bears",
+    "56beb97c3aeaaa14008c92dd": "linebacker",
+    "56beb97c3aeaaa14008c92de": "Elway",
+    "56beb97c3aeaaa14008c92df": "Chicago Bears",
+    "56bf6c3e3aeaaa14008c9615": "linebacker",
+    "56bf6c3e3aeaaa14008c9617": "Elway",
+    "56d709ef0d65d21400198306": "Kubiak",
+    "56d709ef0d65d21400198307": "Super Bowl XX",
+    "56beba103aeaaa14008c92e5": "Justin Tucker",
+    "56beba103aeaaa14008c92e6": "419",
+    "56beba103aeaaa14008c92e7": "Ed Mangan",
+    "56beba103aeaaa14008c92e8": "Baltimore Ravens",
+    "56beba103aeaaa14008c92e9": "kicker",
+    "56bf467d3aeaaa14008c95a5": "Justin Tucker",
+    "56bf467d3aeaaa14008c95a6": "kicker",
+    "56bf467d3aeaaa14008c95a7": "a hybrid Bermuda 419 turf",
+    "56d70adc0d65d21400198311": "Justin Tucker",
+    "56d70adc0d65d21400198312": "re-sodded the field with a new playing surface",
+    "56d70adc0d65d21400198313": "a hybrid Bermuda 419 turf",
+    "56d70adc0d65d21400198314": "cleats",
+    "56d9a419dc89441400fdb678": "Justin Tucker",
+    "56d9a419dc89441400fdb679": "natural grass",
+    "56bebad93aeaaa14008c92f9": "Pittsburgh Steelers",
+    "56bebad93aeaaa14008c92fa": "34\u201319",
+    "56bebad93aeaaa14008c92fb": "Denver",
+    "56bebad93aeaaa14008c92fc": "white",
+    "56bebad93aeaaa14008c92fd": "Super Bowl XXXIII",
+    "56bf48cc3aeaaa14008c95ab": "Super Bowl XXXIII",
+    "56bf48cc3aeaaa14008c95ac": "34\u201319",
+    "56bf48cc3aeaaa14008c95ae": "Atlanta Falcons",
+    "56bf48cc3aeaaa14008c95af": "white",
+    "56d70ccc0d65d2140019831d": "road white jerseys with matching white pants",
+    "56d70ccc0d65d2140019831f": "Pittsburgh Steelers",
+    "56d9a637dc89441400fdb697": "Super Bowl XXXIII",
+    "56d9a637dc89441400fdb698": "white",
+    "56d9a637dc89441400fdb699": "orange",
+    "56d9a637dc89441400fdb69a": "black jerseys with silver pants",
+    "56bebb293aeaaa14008c9303": "San Jose State",
+    "56bebb293aeaaa14008c9304": "Stanford",
+    "56bebb293aeaaa14008c9305": "San Jose",
+    "56bebb293aeaaa14008c9306": "Santa Clara",
+    "56bf49993aeaaa14008c95b5": "San Jose Marriott",
+    "56bf49993aeaaa14008c95b6": "San Jose Marriott",
+    "56bf49993aeaaa14008c95b7": "San Jose State practice facility",
+    "56bf49993aeaaa14008c95b8": "Stanford University",
+    "56d70d240d65d21400198326": "San Jose State practice facility",
+    "56d70d240d65d21400198327": "San Jose Marriott",
+    "56d70d240d65d21400198328": "Stanford University",
+    "56d70d240d65d21400198329": "Santa Clara Marriott",
+    "56d9a6b4dc89441400fdb6a0": "San Jose State practice facility",
+    "56d9a6b4dc89441400fdb6a1": "San Jose Marriott",
+    "56d9a6b4dc89441400fdb6a2": "Stanford University",
+    "56d9a6b4dc89441400fdb6a3": "Santa Clara Marriott",
+    "56bebbbf3aeaaa14008c9315": "June 4, 2014",
+    "56bebbbf3aeaaa14008c9316": "Super Bowl V",
+    "56bebbbf3aeaaa14008c9317": "Jaime Weston",
+    "56bebbbf3aeaaa14008c9318": "Super Bowl XLV",
+    "56bebbbf3aeaaa14008c9319": "Vince Lombardi",
+    "56bf4e1b3aeaaa14008c95bd": "2014",
+    "56bf4e1b3aeaaa14008c95be": "Super Bowl LI",
+    "56bf4e1b3aeaaa14008c95c0": "Super Bowl 50",
+    "56bf4e1b3aeaaa14008c95c1": "gold",
+    "56d70daa0d65d21400198332": "June 4, 2014",
+    "56d70daa0d65d21400198333": "Arabic numerals",
+    "56d70daa0d65d21400198334": "Super Bowl L",
+    "56d70daa0d65d21400198335": "gold",
+    "56d70daa0d65d21400198336": "Super Bowl LI",
+    "56d9a7addc89441400fdb6a8": "Roman",
+    "56d9a7addc89441400fdb6ab": "Super Bowl 50 as opposed to Super Bowl L",
+    "56bebc383aeaaa14008c931f": "gold",
+    "56bebc383aeaaa14008c9320": "week 7",
+    "56bebc383aeaaa14008c9321": "50-yard line",
+    "56bf52483aeaaa14008c95c7": "gold",
+    "56bf52483aeaaa14008c95c8": "Gold",
+    "56d70e570d65d2140019833c": "Golden Super Bowl",
+    "56d70e570d65d2140019833e": "Gold footballs",
+    "56d70e570d65d2140019833f": "50",
+    "56d9b01fdc89441400fdb6d4": "gold",
+    "56bebcbe3aeaaa14008c9325": "Moscone Center",
+    "56bebcbe3aeaaa14008c9326": "San Francisco",
+    "56bebcbe3aeaaa14008c9327": "Ed Lee",
+    "56bebcbe3aeaaa14008c9328": "Jane Kim",
+    "56bebcbe3aeaaa14008c9329": "January 30",
+    "56bf53e73aeaaa14008c95cb": "More than 1 million",
+    "56bf53e73aeaaa14008c95cc": "Ed Lee",
+    "56bf53e73aeaaa14008c95cd": "Moscone Center",
+    "56bf53e73aeaaa14008c95ce": "Super Bowl City",
+    "56d7145c0d65d2140019834c": "Moscone Center",
+    "56d7145c0d65d2140019834d": "Super Bowl City",
+    "56d7145c0d65d2140019834f": "Ed Lee",
+    "56d9b389dc89441400fdb6ec": "Super Bowl City",
+    "56d9b389dc89441400fdb6ed": "More than 1 million",
+    "56d9b389dc89441400fdb6ee": "Ed Lee",
+    "56d9b389dc89441400fdb6ef": "$5 million",
+    "56d9b389dc89441400fdb6f0": "NFL Experience",
+    "56bebd713aeaaa14008c932f": "Santa Clara University",
+    "56bebd713aeaaa14008c9330": "$2 million",
+    "56bebd713aeaaa14008c9331": "week",
+    "56bf555e3aeaaa14008c95d3": "$2 million",
+    "56bf555e3aeaaa14008c95d4": "pep rally",
+    "56bf555e3aeaaa14008c95d5": "city council",
+    "56d714cd0d65d21400198356": "Bellomy Field",
+    "56d714cd0d65d21400198358": "A professional fundraiser",
+    "56d714cd0d65d21400198359": "the city council",
+    "56d9b43edc89441400fdb700": "$2 million",
+    "56d9b43edc89441400fdb701": "the city council",
+    "56bebde53aeaaa14008c9335": "February 1, 2016",
+    "56bebde53aeaaa14008c9336": "Tuesday",
+    "56bebde53aeaaa14008c9337": "SAP Center",
+    "56bebde53aeaaa14008c9338": "San Jose",
+    "56bebde53aeaaa14008c9339": "Golden Gate Bridge",
+    "56bf57043aeaaa14008c95d9": "Tuesday",
+    "56bf57043aeaaa14008c95da": "Tuesday",
+    "56bf57043aeaaa14008c95db": "Super Bowl Opening Night",
+    "56bf57043aeaaa14008c95dc": "SAP Center",
+    "56bf57043aeaaa14008c95dd": "San Jose",
+    "56d71bd80d65d2140019835e": "Tuesday",
+    "56d71bd80d65d21400198360": "Super Bowl Opening Night",
+    "56d71bd80d65d21400198361": "SAP Center in San Jose",
+    "56d71bd80d65d21400198362": "Golden Gate Bridge",
+    "56d9b4ebdc89441400fdb708": "Monday",
+    "56d9b4ebdc89441400fdb709": "Super Bowl Opening Night",
+    "56d9b4ebdc89441400fdb70a": "SAP Center in San Jose",
+    "56d9b4ebdc89441400fdb70b": "Golden Gate Bridge",
+    "56d9b4ebdc89441400fdb70c": "February 1, 2016",
+    "56bebe873aeaaa14008c933f": "Business Connect",
+    "56bebe873aeaaa14008c9340": "over $40 million",
+    "56bebe873aeaaa14008c9341": "Dignity",
+    "56bebe873aeaaa14008c9342": "Apple, Google, Yahoo!",
+    "56bebe873aeaaa14008c9343": "Chevron",
+    "56bf59523aeaaa14008c95e3": "Business Connect",
+    "56bf59523aeaaa14008c95e4": "over $40 million",
+    "56bf59523aeaaa14008c95e5": "through sponsors including Apple, Google, Yahoo!, Intel, Gap, Chevron, and Dignity Health",
+    "56d71cb60d65d21400198368": "Business Connect",
+    "56d9b5ffdc89441400fdb720": "Business Connect",
+    "56d9b5ffdc89441400fdb721": "over $40 million",
+    "56bebec43aeaaa14008c9349": "25",
+    "56bebec43aeaaa14008c934a": "the 50 fund",
+    "56bf5abc3aeaaa14008c95e9": "25",
+    "56bf5abc3aeaaa14008c95ea": "the 50 fund",
+    "56d71d150d65d2140019836e": "giving",
+    "56d71d150d65d2140019836f": "25",
+    "56d71d150d65d21400198370": "the 50 fund",
+    "56d9b6a3dc89441400fdb72f": "25",
+    "56d9b6a3dc89441400fdb730": "the 50 fund",
+    "56bec0353aeaaa14008c934d": "Vince Lombardi",
+    "56bec0353aeaaa14008c934e": "18",
+    "56bec0353aeaaa14008c934f": "33",
+    "56bec0353aeaaa14008c9350": "Tiffany & Co",
+    "56bec0353aeaaa14008c9351": "Tiffany & Co",
+    "56bf5b933aeaaa14008c95ed": "Vince Lombardi Trophy",
+    "56bf5b933aeaaa14008c95ef": "18-karat gold",
+    "56bf5b933aeaaa14008c95f1": "Tiffany & Co",
+    "56d71d9d0d65d21400198374": "Vince Lombardi Trophy",
+    "56d71d9d0d65d21400198377": "Tiffany & Co",
+    "56bec0dd3aeaaa14008c9357": "CBS",
+    "56bec0dd3aeaaa14008c9358": "Phil Simms",
+    "56bec0dd3aeaaa14008c9359": "Tracy Wolfson",
+    "56bec0dd3aeaaa14008c935a": "36",
+    "56bec0dd3aeaaa14008c935b": "5K",
+    "56bf5e7b3aeaaa14008c95f7": "three",
+    "56bf5e7b3aeaaa14008c95f8": "on the sidelines",
+    "56bf5e7b3aeaaa14008c95f9": "360-degree",
+    "56bf5e7b3aeaaa14008c95fa": "5K",
+    "56bf5e7b3aeaaa14008c95fb": "the sidelines",
+    "56d71ed50d65d2140019837c": "CBS",
+    "56d71ed50d65d2140019837d": "three",
+    "56d9b7dcdc89441400fdb740": "CBS",
+    "56d9b7dcdc89441400fdb741": "Jim Nantz and Phil Simms",
+    "56d9b7dcdc89441400fdb742": "Tracy Wolfson and Evan Washburn",
+    "56d9b7dcdc89441400fdb743": "5K",
+    "56d9b7dcdc89441400fdb744": "5K resolution",
+    "56bec1823aeaaa14008c9361": "NBC and Fox",
+    "56bec1823aeaaa14008c9362": "John Sutcliffe",
+    "56bec1823aeaaa14008c9363": "Alvaro Martin",
+    "56bec1823aeaaa14008c9364": "December 28, 2015",
+    "56bec1823aeaaa14008c9365": "Spanish",
+    "56bf6d343aeaaa14008c961b": "NBC and Fox",
+    "56d71fc00d65d21400198386": "ESPN Deportes",
+    "56d71fc00d65d21400198389": "John Sutcliffe",
+    "56d9ba95dc89441400fdb754": "ESPN Deportes",
+    "56d9ba95dc89441400fdb757": "Alvaro Martin and Raul Allegre",
+    "56d9ba95dc89441400fdb758": "John Sutcliffe",
+    "56bec2013aeaaa14008c9371": "NFL Mobile",
+    "56bec2013aeaaa14008c9372": "WatchESPN",
+    "56bec2013aeaaa14008c9373": "CBSSports.com",
+    "56bec2013aeaaa14008c9374": "tablets, Windows 10, Xbox One",
+    "56bec2013aeaaa14008c9375": "Windows 10",
+    "56bf6e7a3aeaaa14008c961f": "CBSSports.com",
+    "56bf6e7a3aeaaa14008c9620": "Xbox One",
+    "56bf6e7a3aeaaa14008c9621": "Verizon Wireless",
+    "56bf6e7a3aeaaa14008c9622": "NFL Mobile",
+    "56d7205e0d65d21400198391": "NFL",
+    "56d7205e0d65d21400198392": "NFL",
+    "56d9bc13dc89441400fdb75e": "digital streams",
+    "56d9bc13dc89441400fdb75f": "NFL",
+    "56d9bc13dc89441400fdb760": "WatchESPN",
+    "56bec2573aeaaa14008c937b": "The Late Show with Stephen Colbert",
+    "56bec2573aeaaa14008c937c": "The Late Late Show with James Corden",
+    "56bf6f743aeaaa14008c9631": "The Late Show with Stephen Colbert",
+    "56bf6f743aeaaa14008c9632": "The Late Show with Stephen Colbert",
+    "56d720c70d65d21400198396": "The Late Show",
+    "56d720c70d65d21400198397": "the game",
+    "56d720c70d65d21400198398": "The Late Late Show with James Corden",
+    "56bec3153aeaaa14008c9389": "$5,000,000",
+    "56bec3153aeaaa14008c938a": "Anheuser-Busch InBev",
+    "56bec3153aeaaa14008c938b": "Doritos",
+    "56bec3153aeaaa14008c938c": "20th",
+    "56bf717d3aeaaa14008c9635": "$5,000,000",
+    "56bf717d3aeaaa14008c9637": "Anheuser-Busch InBev",
+    "56bf717d3aeaaa14008c9638": "Doritos",
+    "56bf717d3aeaaa14008c9639": "Nintendo and The Pok\u00e9mon",
+    "56d721af0d65d2140019839e": "Pok\u00e9mon Company",
+    "56d721af0d65d2140019839f": "Anheuser-Busch InBev",
+    "56d721af0d65d214001983a0": "Doritos",
+    "56d9bdc1dc89441400fdb768": "Anheuser-Busch InBev",
+    "56d9bdc1dc89441400fdb769": "Doritos",
+    "56d9bdc1dc89441400fdb76a": "Crash the Super Bowl",
+    "56bec38b3aeaaa14008c9397": "Small Business Big Game",
+    "56bec38b3aeaaa14008c9398": "Death Wish Coffee",
+    "56bec38b3aeaaa14008c9399": "30-second",
+    "56bec38b3aeaaa14008c939a": "nine",
+    "56bf71e73aeaaa14008c963f": "Death Wish Coffee",
+    "56bf71e73aeaaa14008c9640": "nine",
+    "56d722330d65d214001983a6": "QuickBooks",
+    "56d722330d65d214001983a7": "Death Wish Coffee",
+    "56d722330d65d214001983a8": "nine",
+    "56d9be16dc89441400fdb770": "Death Wish Coffee",
+    "56d9be16dc89441400fdb771": "Death Wish Coffee",
+    "56bec4343aeaaa14008c93a9": "Jason Bourne",
+    "56bec4343aeaaa14008c93aa": "Gods of Egypt",
+    "56bec4343aeaaa14008c93ab": "Teenage Mutant Ninja Turtles: Out of the Shadows",
+    "56bec4343aeaaa14008c93ac": "Resurgence",
+    "56d723560d65d214001983ac": "Gods of Egypt",
+    "56d723560d65d214001983ad": "Teenage Mutant Ninja Turtles: Out of the Shadows",
+    "56d723560d65d214001983ae": "Jason Bourne",
+    "56d723560d65d214001983af": "Captain America: Civil War",
+    "56d9bf70dc89441400fdb77b": "Deadpool",
+    "56d9bf70dc89441400fdb77c": "20th Century Fox",
+    "56d9bf70dc89441400fdb77d": "Lionsgate",
+    "56bec4a33aeaaa14008c93b1": "Westwood One",
+    "56bec4a33aeaaa14008c93b2": "Kevin Harlan",
+    "56bec4a33aeaaa14008c93b3": "Jim Gray",
+    "56bec4a33aeaaa14008c93b4": "Boomer Esiason",
+    "56bec4a33aeaaa14008c93b5": "James Lofton",
+    "56bf738b3aeaaa14008c9655": "Boomer Esiason and Dan Fouts",
+    "56bf738b3aeaaa14008c9656": "Kevin Harlan",
+    "56d723ad0d65d214001983b4": "Westwood One",
+    "56d723ad0d65d214001983b5": "Kevin Harlan",
+    "56d723ad0d65d214001983b8": "Jim Gray",
+    "56d9c049dc89441400fdb78e": "Jim Gray",
+    "56d9c049dc89441400fdb78f": "Boomer Esiason and Dan Fouts",
+    "56d9c049dc89441400fdb790": "James Lofton and Mark Malone",
+    "56d9c049dc89441400fdb791": "pre-game and halftime coverage",
+    "56d9c049dc89441400fdb792": "North America",
+    "56bec5233aeaaa14008c93cf": "KRFX",
+    "56bec5233aeaaa14008c93d0": "Mick Mixon",
+    "56bec5233aeaaa14008c93d1": "1110 AM",
+    "56bec5233aeaaa14008c93d2": "Chester",
+    "56bec5233aeaaa14008c93d3": "Mick Mixon",
+    "56d724620d65d214001983be": "Dave Logan",
+    "56d724620d65d214001983bf": "Ed McCaffrey",
+    "56d724620d65d214001983c0": "WBT",
+    "56d724620d65d214001983c1": "Mick Mixon",
+    "56d9c203dc89441400fdb7a0": "KOA (850 AM) and KRFX",
+    "56d9c203dc89441400fdb7a3": "WBT",
+    "56bec5ff3aeaaa14008c93e3": "BBC Radio 5 Live",
+    "56bec5ff3aeaaa14008c93e4": "BBC Radio 5 Live",
+    "56bec5ff3aeaaa14008c93e5": "Darren Fletcher",
+    "56d724ea0d65d214001983c8": "The BBC",
+    "56d724ea0d65d214001983c9": "Greg Brady",
+    "56bec6573aeaaa14008c93e9": "Bart Starr",
+    "56bec6573aeaaa14008c93ea": "Chuck Howley",
+    "56bec6573aeaaa14008c93eb": "Peyton Manning",
+    "56bec6573aeaaa14008c93ec": "2001",
+    "56bec6573aeaaa14008c93ed": "Peyton Manning",
+    "56d7251d0d65d214001983cc": "39",
+    "56d7251d0d65d214001983cd": "Peyton Manning",
+    "56d7251d0d65d214001983cf": "Harvey Martin",
+    "56d9c3a6dc89441400fdb7b4": "43",
+    "56d9c3a6dc89441400fdb7b5": "43",
+    "56d9c3a6dc89441400fdb7b6": "Bart Starr",
+    "56d9c3a6dc89441400fdb7b7": "Peyton Manning",
+    "56d9c3a6dc89441400fdb7b8": "Harvey Martin",
+    "56bec6ac3aeaaa14008c93fd": "Six",
+    "56bec6ac3aeaaa14008c93fe": "national anthem",
+    "56bec6ac3aeaaa14008c93ff": "Academy Award",
+    "56bec6ac3aeaaa14008c9400": "American Sign Language",
+    "56bec6ac3aeaaa14008c9401": "American Sign Language",
+    "56d20650e7d4791d00902614": "Lady Gaga",
+    "56d20650e7d4791d00902615": "Marlee Matlin",
+    "56d7253b0d65d214001983d4": "Lady Gaga",
+    "56d7253b0d65d214001983d5": "Marlee Matlin",
+    "56d9c455dc89441400fdb7c4": "Lady Gaga",
+    "56d9c455dc89441400fdb7c5": "Six",
+    "56d9c455dc89441400fdb7c6": "Marlee Matlin",
+    "56bec7353aeaaa14008c940d": "December 3",
+    "56bec7353aeaaa14008c940e": "British",
+    "56bec7353aeaaa14008c940f": "Super Bowl XLVII",
+    "56bec7353aeaaa14008c9410": "Hymn for the Weekend",
+    "56bec7353aeaaa14008c9411": "Super Bowl XLVIII",
+    "56d725790d65d214001983d8": "Bruno Mars",
+    "56d725790d65d214001983da": "Pepsi",
+    "56d725790d65d214001983db": "Hymn for the Weekend",
+    "56d9c551dc89441400fdb7d0": "Bruno Mars",
+    "56d9c551dc89441400fdb7d1": "Beyonc\u00e9",
+    "56d9c551dc89441400fdb7d2": "Hymn for the Weekend",
+    "56d9c551dc89441400fdb7d3": "Bruno Mars, who headlined the Super Bowl XLVIII halftime show, and Mark Ronson",
+    "56bec7a63aeaaa14008c9417": "Denver",
+    "56bec7a63aeaaa14008c9418": "Andre Caldwell",
+    "56bec7a63aeaaa14008c9419": "Ronnie Hillman",
+    "56bec7a63aeaaa14008c941a": "Brandon McManus",
+    "56bec7a63aeaaa14008c941b": "C. J. Anderson",
+    "56d728a10d65d21400198413": "18",
+    "56d728a10d65d21400198414": "Shaq Thompson",
+    "56d728a10d65d21400198415": "Brandon McManus",
+    "56d728a10d65d21400198416": "a deficit",
+    "56d9c5e7dc89441400fdb7d8": "Denver",
+    "56d9c5e7dc89441400fdb7d9": "Owen Daniels",
+    "56d9c5e7dc89441400fdb7da": "C. J. Anderson",
+    "56d9c5e7dc89441400fdb7db": "Shaq Thompson",
+    "56d9c5e7dc89441400fdb7dc": "a deficit",
+    "56bec8243aeaaa14008c942b": "Mike Carey",
+    "56bec8243aeaaa14008c942c": "Cam Newton",
+    "56bec8243aeaaa14008c942d": "Von Miller",
+    "56bec8243aeaaa14008c942e": "Malik Jackson",
+    "56bec8243aeaaa14008c942f": "Super Bowl XXVIII",
+    "56d728e00d65d2140019841c": "Cam Newton",
+    "56d728e00d65d2140019841d": "Mike Carey",
+    "56d728e00d65d2140019841e": "Von Miller",
+    "56d728e00d65d2140019841f": "Malik Jackson",
+    "56d728e00d65d21400198420": "1993",
+    "56d9c660dc89441400fdb7e7": "Mike Carey",
+    "56d9c660dc89441400fdb7e8": "Von Miller",
+    "56d9c660dc89441400fdb7e9": "Malik Jackson",
+    "56d9c660dc89441400fdb7ea": "1993",
+    "56bec8a13aeaaa14008c9435": "Jonathan Stewart",
+    "56bec8a13aeaaa14008c9436": "Jordan Norwood",
+    "56bec8a13aeaaa14008c9437": "28",
+    "56bec8a13aeaaa14008c9438": "61",
+    "56bec8a13aeaaa14008c9439": "33",
+    "56d729180d65d21400198426": "51",
+    "56d729180d65d21400198427": "Jonathan Stewart",
+    "56d729180d65d21400198428": "11:28",
+    "56d729180d65d21400198429": "Jordan Norwood",
+    "56d729180d65d2140019842a": "33",
+    "56d9c6e0dc89441400fdb7f0": "Jonathan Stewart",
+    "56d9c6e0dc89441400fdb7f4": "they could not get the ball into the end zone",
+    "56bec9133aeaaa14008c9445": "Darian Stewart",
+    "56bec9133aeaaa14008c9446": "linebacker",
+    "56bec9133aeaaa14008c9447": "Kony Ealy",
+    "56bec9133aeaaa14008c9448": "DeMarcus Ware",
+    "56bec9133aeaaa14008c9449": "DeMarcus Ware",
+    "56d729ec0d65d21400198430": "Mike Tolbert",
+    "56d729ec0d65d21400198431": "Kony Ealy",
+    "56d729ec0d65d21400198432": "19",
+    "56d729ec0d65d21400198434": "Kony Ealy",
+    "56d9c79edc89441400fdb804": "Mike Tolbert",
+    "56d9c79edc89441400fdb805": "Danny Trevathan",
+    "56d9c79edc89441400fdb806": "Kony Ealy",
+    "56d9c79edc89441400fdb807": "returning the ball 19 yards to the Panthers 39-yard line with 1:55 left on the clock",
+    "56d9c79edc89441400fdb808": "DeMarcus Ware",
+    "56bec98e3aeaaa14008c9455": "Ted Ginn Jr.",
+    "56bec98e3aeaaa14008c9456": "McManus",
+    "56bec98e3aeaaa14008c9457": "33",
+    "56bec98e3aeaaa14008c9458": "McManus",
+    "56bec98e3aeaaa14008c9459": "T. J. Ward",
+    "56d7282f0d65d21400198408": "Ted Ginn Jr.",
+    "56d7282f0d65d21400198409": "the uprights",
+    "56d7282f0d65d2140019840a": "T. J. Ward",
+    "56d7282f0d65d2140019840c": "Trevathan",
+    "56d9c92bdc89441400fdb80e": "Ted Ginn Jr.",
+    "56d9c92bdc89441400fdb80f": "26",
+    "56d9c92bdc89441400fdb810": "Graham Gano",
+    "56d9c92bdc89441400fdb811": "Emmanuel Sanders",
+    "56bec9e83aeaaa14008c945f": "Ealy",
+    "56bec9e83aeaaa14008c9460": "39",
+    "56bec9e83aeaaa14008c9461": "Devin Funchess",
+    "56bec9e83aeaaa14008c9462": "Stewart",
+    "56d7261e0d65d214001983e0": "41",
+    "56d7261e0d65d214001983e1": "Ealy",
+    "56d7261e0d65d214001983e2": "50-yard line",
+    "56d7261e0d65d214001983e4": "punts",
+    "56d9ca0adc89441400fdb820": "Ealy",
+    "56d9ca0adc89441400fdb821": "41",
+    "56d9ca0adc89441400fdb822": "39",
+    "56d9ca0adc89441400fdb823": "three",
+    "56beca913aeaaa14008c946d": "24",
+    "56beca913aeaaa14008c946e": "Miller stripped the ball away from Newton",
+    "56beca913aeaaa14008c946f": "Josh Norman",
+    "56beca913aeaaa14008c9470": "Miller",
+    "56beca913aeaaa14008c9471": "Bennie Fowler",
+    "56d726b60d65d214001983ea": "Miller",
+    "56d726b60d65d214001983eb": "Ward",
+    "56d726b60d65d214001983ec": "Newton",
+    "56d726b60d65d214001983ed": "Josh Norman",
+    "56d726b60d65d214001983ee": "3:08",
+    "56d9cb47dc89441400fdb832": "4:51",
+    "56d9cb47dc89441400fdb833": "Miller",
+    "56d9cb47dc89441400fdb834": "Ward",
+    "56d9cb47dc89441400fdb835": "three",
+    "56d9cb47dc89441400fdb836": "Anderson",
+    "56becaf93aeaaa14008c9481": "five",
+    "56becaf93aeaaa14008c9482": "zero",
+    "56becaf93aeaaa14008c9483": "four",
+    "56becaf93aeaaa14008c9484": "Thomas Davis",
+    "56becaf93aeaaa14008c9485": "18",
+    "56d7270c0d65d214001983f4": "one",
+    "56d7270c0d65d214001983f5": "zero",
+    "56d7270c0d65d214001983f6": "Anderson",
+    "56d7270c0d65d214001983f7": "Sanders",
+    "56d7270c0d65d214001983f8": "Thomas Davis",
+    "56d9ccacdc89441400fdb842": "Sanders",
+    "56d9ccacdc89441400fdb843": "Anderson",
+    "56d9ccacdc89441400fdb844": "11",
+    "56d9ccacdc89441400fdb845": "18",
+    "56d9ccacdc89441400fdb846": "four",
+    "56becb823aeaaa14008c948b": "315 to 194",
+    "56becb823aeaaa14008c948c": "21 to 11",
+    "56becb823aeaaa14008c948d": "Baltimore Ravens",
+    "56becb823aeaaa14008c948e": "Jordan Norwood",
+    "56becb823aeaaa14008c948f": "Gary Kubiak",
+    "56d7277c0d65d214001983fe": "315 to 194",
+    "56d7277c0d65d214001983ff": "21 to 11",
+    "56d7277c0d65d21400198401": "Chicago Bears",
+    "56d7277c0d65d21400198402": "Baltimore Ravens",
+    "56d9cd9adc89441400fdb84c": "21 to 11",
+    "56d9cd9adc89441400fdb84d": "21 to 11",
+    "56d9cd9adc89441400fdb84e": "Baltimore Ravens",
+    "56d9cd9adc89441400fdb84f": "Chicago Bears",
+    "56d9cd9adc89441400fdb850": "seven",
+    "5733a5f54776f41900660f45": "Nobel Prize",
+    "5733a5f54776f41900660f48": "1745",
+    "5733a5f54776f41900660f44": "Maria Sk\u0142odowska-Curie",
+    "5733a5f54776f41900660f46": "W\u0142adys\u0142aw Szpilman",
+    "5733a5f54776f41900660f47": "seven",
+    "57336755d058e614000b5a3d": "100",
+    "57336755d058e614000b5a3f": "Krasi\u0144ski Palace Garden",
+    "57336755d058e614000b5a3c": "The Saxon Garden",
+    "57336755d058e614000b5a3e": "east",
+    "57336755d058e614000b5a40": "\u0141azienki Park",
+    "57337ddc4776f41900660bbb": "15 kilometres",
+    "57337ddc4776f41900660bbc": "otter, beaver and hundreds of bird species",
+    "57337ddc4776f41900660bba": "13",
+    "57337ddc4776f41900660bbd": "several",
+    "57337ddc4776f41900660bbe": "to clean them of plants and sediments",
+    "57337ea24776f41900660bd0": "foreign-born inhabitants",
+    "57337ea24776f41900660bd3": "833,500",
+    "57337ea24776f41900660bd2": "34%",
+    "57337ea24776f41900660bd1": "Jewish",
+    "57337ea24776f41900660bd4": "internal migration and urbanisation",
+    "57339555d058e614000b5df4": "Warsaw University of Technology",
+    "57339555d058e614000b5df5": "2,000",
+    "57339555d058e614000b5df6": "Medical University of Warsaw",
+    "57339555d058e614000b5df3": "1816",
+    "57339555d058e614000b5df7": "Fryderyk Chopin University of Music",
+    "57339902d058e614000b5e70": "1816",
+    "57339902d058e614000b5e71": "over two million",
+    "57339902d058e614000b5e72": "architects",
+    "57339902d058e614000b5e73": "Irena Bajerska",
+    "57339902d058e614000b5e74": "5,111 m2 (55,014.35 sq ft)",
+    "573399b54776f41900660e64": "infrastructure",
+    "573399b54776f41900660e65": "the initial Three-Year Plan",
+    "573399b54776f41900660e66": "solid economic growth",
+    "573399b54776f41900660e67": "improved markedly",
+    "57339a554776f41900660e74": "Warsaw",
+    "57339a554776f41900660e75": "Children's Memorial Health Institute",
+    "57339a554776f41900660e76": "Maria Sk\u0142odowska-Curie Institute of Oncology",
+    "57339a554776f41900660e77": "700",
+    "57339a554776f41900660e78": "developed",
+    "57339ad74776f41900660e86": "musical",
+    "57339ad74776f41900660e87": "events and festivals",
+    "57339ad74776f41900660e88": "Palace of Culture and Science",
+    "57339ad74776f41900660e89": "Warsaw Summer Jazz Days",
+    "57339ad74776f41900660e8a": "events and festivals",
+    "57339c16d058e614000b5ec5": "Ogr\u00f3d Saski",
+    "57339c16d058e614000b5ec6": "Ogr\u00f3d Saski",
+    "57339c16d058e614000b5ec7": "from 1870 to 1939",
+    "57339c16d058e614000b5ec8": "Momus",
+    "57339c16d058e614000b5ec9": "Wojciech Bogus\u0142awski Theatre",
+    "57339dd94776f41900660ecc": "Wianki",
+    "57339dd94776f41900660ecd": "thousands",
+    "57339dd94776f41900660ece": "Midsummer\u2019s Night",
+    "57339dd94776f41900660ecf": "when they would be married",
+    "57339dd94776f41900660ed0": "fern",
+    "57339eb9d058e614000b5ef6": "art posters",
+    "57339eb9d058e614000b5ef7": "60",
+    "57339eb9d058e614000b5ef8": "prestigious",
+    "57339eb9d058e614000b5ef9": "paintings",
+    "57339eb9d058e614000b5efa": "the history of arms",
+    "5733a1854776f41900660f0a": "Warsaw Uprising Museum",
+    "5733a1854776f41900660f0b": "Katy\u0144 Museum",
+    "5733a1854776f41900660f0c": "stereoscopic theatre",
+    "5733a1854776f41900660f0d": "Warsaw Fotoplastikon",
+    "5733a1854776f41900660f0e": "60",
+    "5733a2a9d058e614000b5f28": "Royal Ujazd\u00f3w Castle",
+    "5733a2a9d058e614000b5f29": "about 500",
+    "5733a2a9d058e614000b5f2a": "Zach\u0119ta National Gallery of Art",
+    "5733a2a9d058e614000b5f2b": "Polish and international artists",
+    "5733a2a9d058e614000b5f2c": "September",
+    "5733a32bd058e614000b5f32": "Polonia Warsaw",
+    "5733a32bd058e614000b5f33": "1946",
+    "5733a32bd058e614000b5f34": "twice",
+    "5733a32bd058e614000b5f35": "Konwiktorska Street",
+    "5733a32bd058e614000b5f36": "disastrous financial situation",
+    "5733a45d4776f41900660f23": "syrenka",
+    "5733a45d4776f41900660f24": "The mermaid",
+    "5733a45d4776f41900660f25": "since at least the mid-14th century",
+    "5733a45d4776f41900660f26": "1390",
+    "5733a45d4776f41900660f27": "a sword",
+    "5733a560d058e614000b5f77": "mermaid",
+    "5733a560d058e614000b5f78": "the depths of the oceans and seas",
+    "5733a560d058e614000b5f79": "the coast of Denmark",
+    "5733a560d058e614000b5f7a": "Warszowa",
+    "5733a560d058e614000b5f7b": "captured",
+    "5733a6ac4776f41900660f58": "Warsaw",
+    "5733a6ac4776f41900660f59": "1916",
+    "5733a6ac4776f41900660f5a": "Art Deco style",
+    "5733a6ac4776f41900660f5b": "Israeli poet",
+    "5733a6ac4776f41900660f5c": "Isaac Bashevis Singer",
+    "573312fcd058e614000b56e6": "Economist Intelligence Unit",
+    "573312fcd058e614000b56e7": "2012",
+    "573312fcd058e614000b56e8": "FMCG manufacturing, metal processing, steel and electronic manufacturing and food processing",
+    "573312fcd058e614000b56e9": "Warsaw Stock Exchange",
+    "573312fcd058e614000b56ea": "Frontex",
+    "5733140a4776f419006606e0": "1313",
+    "5733140a4776f419006606e1": "Krak\u00f3w",
+    "5733140a4776f419006606e2": "1596",
+    "5733140a4776f419006606e3": "King Sigismund III Vasa",
+    "5733140a4776f419006606e4": "it has survived many wars, conflicts and invasions throughout its long history",
+    "573314e3d058e614000b56f0": "Roman Catholic",
+    "573314e3d058e614000b56f1": "Polish Academy of Sciences",
+    "573314e3d058e614000b56f2": "UNESCO World Heritage Site",
+    "573314e3d058e614000b56f3": "architectural",
+    "573314e3d058e614000b56f4": "luxurious parks and royal gardens",
+    "57332442d058e614000b5720": "Warszawa",
+    "57332442d058e614000b5721": "belonging to Warsz",
+    "57332442d058e614000b5722": "a 12th/13th-century nobleman",
+    "57332442d058e614000b5723": "a village located at the modern-day site of Mariensztat neighbourhood",
+    "57332442d058e614000b5724": "miasto sto\u0142eczne Warszawa",
+    "57332562d058e614000b5730": "Jazd\u00f3w",
+    "57332562d058e614000b5731": "The Prince of P\u0142ock",
+    "57332562d058e614000b5732": "1300",
+    "57332562d058e614000b5733": "1413",
+    "57332562d058e614000b5734": "1526",
+    "5733266d4776f41900660712": "General Sejm",
+    "5733266d4776f41900660713": "1569",
+    "5733266d4776f41900660714": "religious freedom in the Polish\u2013Lithuanian Commonwealth",
+    "5733266d4776f41900660715": "its central location between the Commonwealth's capitals of Krak\u00f3w and Vilnius",
+    "5733266d4776f41900660716": "1596",
+    "57332a734776f41900660726": "until 1796",
+    "57332a734776f41900660727": "Kingdom of Prussia",
+    "57332a734776f41900660728": "Napoleon's army",
+    "57332a734776f41900660729": "Following the Congress of Vienna of 1815",
+    "57332a734776f4190066072a": "1816",
+    "57332b66d058e614000b5758": "from 4 August 1915 until November 1918",
+    "57332b66d058e614000b5759": "areas controlled by Russia",
+    "57332b66d058e614000b575a": "Pi\u0142sudski",
+    "57332b66d058e614000b575b": "1920",
+    "57332b66d058e614000b575c": "Red Army",
+    "57332c1e4776f4190066073a": "1 September 1939",
+    "57332c1e4776f4190066073b": "German Nazi colonial administration",
+    "57332c1e4776f4190066073c": "several hundred thousand",
+    "57332c1e4776f4190066073d": "19 April 1943",
+    "57332c1e4776f4190066073e": "almost a month",
+    "57332e48d058e614000b5762": "the Red Army",
+    "57332e48d058e614000b5763": "Knowing that Stalin was hostile to the idea of an independent Poland",
+    "57332e48d058e614000b5764": "1 August 1944",
+    "57332e48d058e614000b5765": "63",
+    "57332e48d058e614000b5766": "between 150,000 and 200,000",
+    "57332f81d058e614000b5776": "Bricks for Warsaw",
+    "57332f81d058e614000b5777": "prefabricated housing projects",
+    "57332f81d058e614000b5778": "Eastern Bloc city",
+    "57332f81d058e614000b5779": "Palace of Culture and Science",
+    "57332f81d058e614000b577a": "UNESCO's World Heritage list",
+    "573330444776f41900660758": "John Paul II",
+    "573330444776f41900660759": "anti-communist fervor",
+    "573330444776f4190066075a": "less than a year",
+    "573330444776f4190066075b": "Victory Square",
+    "573330444776f4190066075c": "the incentive for the democratic changes",
+    "5733314e4776f4190066076a": "300",
+    "5733314e4776f4190066076b": "325",
+    "5733314e4776f4190066076c": "Vistula River",
+    "5733314e4776f4190066076d": "115.7 metres",
+    "5733314e4776f4190066076e": "the right bank of the Vistula",
+    "57335c20d058e614000b58f9": "two",
+    "57335c20d058e614000b58fa": "the plain moraine plateau and the Vistula Valley",
+    "57335c20d058e614000b58fc": "moraine plateau",
+    "57335c20d058e614000b58fb": "The Vistula River",
+    "57335c20d058e614000b58fd": "Warsaw Escarpment",
+    "57335ddbd058e614000b592f": "plain moraine plateau",
+    "57335ddbd058e614000b5930": "former flooded terraces",
+    "57335ddbd058e614000b5931": "valleys and ground depressions",
+    "57335ddbd058e614000b5932": "highest terrace",
+    "57335ddbd058e614000b5933": "pine forest",
+    "57335fcad058e614000b5970": "the turbulent history of the city and country",
+    "57335fcad058e614000b5971": "Second World War",
+    "57335fcad058e614000b5972": "After liberation",
+    "57335fcad058e614000b5973": "Leopold Kronenberg Palace",
+    "57335fcad058e614000b5974": "Eastern bloc countries",
+    "573361404776f4190066093c": "Gothic",
+    "573361404776f4190066093d": "14th century",
+    "573361404776f4190066093e": "Masovian gothic style",
+    "573361404776f4190066093f": "Renaissance",
+    "573361404776f41900660940": "mannerist architecture",
+    "573362b94776f41900660974": "17th century",
+    "573362b94776f41900660975": "1688\u20131692",
+    "573362b94776f41900660976": "rococo",
+    "573362b94776f41900660977": "neoclassical architecture",
+    "573362b94776f41900660978": "1775\u20131795",
+    "5733638fd058e614000b59e8": "bourgeois architecture",
+    "5733638fd058e614000b59e9": "Exceptional examples of the bourgeois architecture of the later periods were not restored by the communist authorities after the war",
+    "5733638fd058e614000b59ea": "socialist realism",
+    "5733638fd058e614000b59eb": "Warsaw University of Technology building",
+    "5733638fd058e614000b59ec": "the most distinctive buildings",
+    "5733647e4776f419006609ae": "places",
+    "5733647e4776f419006609af": "Pawiak",
+    "5733647e4776f419006609b0": "Warsaw Citadel",
+    "5733647e4776f419006609b1": "the children who served as messengers and frontline troops in the Warsaw Uprising",
+    "5733647e4776f419006609b2": "Warsaw Uprising Monument",
+    "573368044776f41900660a29": "green",
+    "573368044776f41900660a2a": "the New Orangery",
+    "573368044776f41900660a2b": "Pole Mokotowskie (a big park in the northern Mokot\u00f3w",
+    "573368044776f41900660a2c": "Park Ujazdowski",
+    "573368044776f41900660a2d": "1927",
+    "573368e54776f41900660a53": "the location of Warsaw within the border region of several big floral regions",
+    "573368e54776f41900660a54": "within the borders of Warsaw",
+    "573368e54776f41900660a55": "Masovian Primeval Forest",
+    "573368e54776f41900660a56": "Kabaty Forest",
+    "573368e54776f41900660a57": "two",
+    "57337f6ad058e614000b5bcb": "1,300,000",
+    "57337f6ad058e614000b5bcc": "420,000",
+    "57337f6ad058e614000b5bcd": "1951",
+    "57337f6ad058e614000b5bce": "better only because they lived in the capital",
+    "57337f6ad058e614000b5bcf": "residency registration",
+    "57338007d058e614000b5bda": "multi-cultural",
+    "57338007d058e614000b5bdb": "711,988",
+    "57338007d058e614000b5bdc": "56.2%",
+    "57338007d058e614000b5bdd": "2.4%",
+    "57338007d058e614000b5bde": "Warsaw Uprising of 1944",
+    "573380e0d058e614000b5be9": "a commune",
+    "573380e0d058e614000b5bea": "counties or powiats",
+    "573380e0d058e614000b5beb": "Krak\u00f3w",
+    "57338160d058e614000b5bf9": "Warsaw City Council",
+    "57338160d058e614000b5bfa": "60",
+    "57338160d058e614000b5bfb": "every four years",
+    "57338160d058e614000b5bfc": "committees",
+    "57338160d058e614000b5bfd": "30",
+    "57338255d058e614000b5c0d": "President",
+    "57338255d058e614000b5c0e": "Jan Andrzej Menich",
+    "57338255d058e614000b5c0f": "1695\u20131696",
+    "57338255d058e614000b5c10": "the City council",
+    "57338255d058e614000b5c11": "Centrum",
+    "573382d24776f41900660c37": "\u015ar\u00f3dmie\u015bcie",
+    "573382d24776f41900660c38": "304,016",
+    "573382d24776f41900660c39": "emerging market",
+    "573382d24776f41900660c3a": "12%",
+    "573382d24776f41900660c3b": "191.766 billion PLN, 111696 PLN per capita",
+    "5733834ed058e614000b5c26": "1817",
+    "5733834ed058e614000b5c27": "World War II",
+    "5733834ed058e614000b5c28": "World War II",
+    "5733834ed058e614000b5c29": "374",
+    "5733834ed058e614000b5c2a": "Polish United Workers' Party (PZPR)",
+    "573383d0d058e614000b5c35": "1951",
+    "573383d0d058e614000b5c36": "Fiat 125p",
+    "573383d0d058e614000b5c37": "Daewoo",
+    "573383d0d058e614000b5c38": "AvtoZAZ",
+    "573383d0d058e614000b5c39": "The license for the production of the Aveo expired in February 2011 and has since not been renewed",
+    "573166ede6313a140071cef2": "Warszawa",
+    "573166ede6313a140071cef3": "Warsaw",
+    "573166ede6313a140071cef4": "Vistula River",
+    "573166ede6313a140071cef5": "2.666 million",
+    "573166ede6313a140071cef6": "9th",
+    "5732b6b5328d981900602021": "Warsaw",
+    "5732b6b5328d981900602022": "Vistula River",
+    "5732b6b5328d981900602023": "260 kilometres",
+    "5732b6b5328d981900602024": "2.666 million",
+    "5732b6b5328d981900602025": "9th",
+    "56ddde6b9a695914005b9628": "France",
+    "56ddde6b9a695914005b9629": "10th and 11th centuries",
+    "56ddde6b9a695914005b962a": "Denmark, Iceland and Norway",
+    "56ddde6b9a695914005b962b": "Rollo",
+    "56ddde6b9a695914005b962c": "10th",
+    "56dddf4066d3e219004dad5f": "William the Conqueror",
+    "56dddf4066d3e219004dad60": "Richard I of Normandy",
+    "56dddf4066d3e219004dad61": "Catholic orthodoxy",
+    "56dde0379a695914005b9636": "\"Norseman, Viking\"",
+    "56dde0379a695914005b9637": "9th century",
+    "56dde0ba66d3e219004dad75": "911",
+    "56dde0ba66d3e219004dad76": "King Charles III of West Francia",
+    "56dde0ba66d3e219004dad77": "river Epte",
+    "56dde1d966d3e219004dad8d": "Rollo",
+    "56dde27d9a695914005b9651": "Christianity",
+    "56dde27d9a695914005b9652": "north",
+    "56dde2fa66d3e219004dad9b": "fighting horsemen",
+    "56dde3aa9a695914005b9660": "999",
+    "56dde3aa9a695914005b9661": "Archangel Michael",
+    "56dde3aa9a695914005b9662": "Monte Gargano",
+    "56de0daecffd8e1900b4b595": "Drogo",
+    "56de0daecffd8e1900b4b596": "prince Guaimar IV of Salerno \"Duke of Apulia and Calabria\". He promptly awarded their elected leader, William Iron Arm",
+    "56de0e25cffd8e1900b4b59a": "the Saracens",
+    "56de0e25cffd8e1900b4b59b": "1130",
+    "56de0e25cffd8e1900b4b59c": "Squillace",
+    "56de0ed14396321400ee2579": "Kitab Rudjdjar",
+    "56de0ed14396321400ee257a": "The Book of Roger",
+    "56de0ed14396321400ee257b": "meritocratic",
+    "56de0f6a4396321400ee257f": "the Pechenegs, the Bulgars, and especially the Seljuk Turks",
+    "56de0ffd4396321400ee258d": "1050s",
+    "56de0ffd4396321400ee258e": "1060s",
+    "56de0ffd4396321400ee258f": "Byzantine general Alexius Komnenos",
+    "56de10b44396321400ee2593": "Afranji",
+    "56de10b44396321400ee2594": "Oursel",
+    "56de10b44396321400ee2595": "Armenians",
+    "56de11154396321400ee25aa": "Italo-Norman",
+    "56de148dcffd8e1900b4b5bc": "Robert Guiscard",
+    "56de148dcffd8e1900b4b5bd": "February 1082",
+    "56de148dcffd8e1900b4b5be": "30,000",
+    "56de15104396321400ee25b7": "Deabolis",
+    "56de15104396321400ee25b8": "Bohemond",
+    "56de15104396321400ee25b9": "river Deabolis",
+    "56de1563cffd8e1900b4b5c2": "1185",
+    "56de1563cffd8e1900b4b5c3": "Dyrrachium",
+    "56de1563cffd8e1900b4b5c4": "the Adriatic",
+    "56de15dbcffd8e1900b4b5c8": "King Ethelred II of England",
+    "56de15dbcffd8e1900b4b5c9": "Duke Richard II of Normandy",
+    "56de15dbcffd8e1900b4b5ca": "Normandy",
+    "56de15dbcffd8e1900b4b5cb": "Sweyn Forkbeard",
+    "56de1645cffd8e1900b4b5d0": "Harthacnut",
+    "56de1645cffd8e1900b4b5d1": "1041",
+    "56de1645cffd8e1900b4b5d2": "Robert of Jumi\u00e8ges",
+    "56de16ca4396321400ee25c5": "Battle of Hastings",
+    "56de16ca4396321400ee25c6": "Duke William II of Normandy",
+    "56de16ca4396321400ee25c7": "1066",
+    "56de16ca4396321400ee25c8": "Anglo-Saxons",
+    "56de1728cffd8e1900b4b5d7": "Modern English",
+    "56de179dcffd8e1900b4b5da": "1169",
+    "56de179dcffd8e1900b4b5db": "Ireland",
+    "56de179dcffd8e1900b4b5dc": "Irish",
+    "56de17f9cffd8e1900b4b5e0": "King Malcolm III of Scotland",
+    "56de17f9cffd8e1900b4b5e1": "King Malcolm III",
+    "56de17f9cffd8e1900b4b5e2": "1072",
+    "56de17f9cffd8e1900b4b5e3": "Duncan",
+    "56de3cd0cffd8e1900b4b6be": "Sybilla of Normandy",
+    "56de3cd0cffd8e1900b4b6bf": "Norman",
+    "56de3d594396321400ee26ca": "Hereford",
+    "56de3d594396321400ee26cb": "the Welsh",
+    "56de3d594396321400ee26cc": "Edward the Confessor",
+    "56de3dbacffd8e1900b4b6d2": "Wales",
+    "56de3e414396321400ee26d8": "1018",
+    "56de3e414396321400ee26d9": "William of Montreuil",
+    "56de3ebc4396321400ee26e6": "1097",
+    "56de3ebc4396321400ee26e7": "Tancred",
+    "56de3ebc4396321400ee26e8": "Jerusalem",
+    "56de3efccffd8e1900b4b6fe": "380 years",
+    "56de3f784396321400ee26fa": "a storm",
+    "56de3f784396321400ee26fb": "Berengaria",
+    "56de3f784396321400ee26fc": "1191",
+    "56de3f784396321400ee26fd": "Richard the Lion-hearted",
+    "56de40da4396321400ee2708": "Conrad of Montferrat",
+    "56de40da4396321400ee2709": "silver",
+    "56de40da4396321400ee270a": "Guy de Lusignan",
+    "56de41504396321400ee2714": "Richard the Lion-Heart married Berengaria of Navarre, first-born daughter of King Sancho VI of Navarre",
+    "56de41504396321400ee2715": "12 May 1191",
+    "56de41504396321400ee2716": "double coronation",
+    "56de48f34396321400ee2770": "1489",
+    "56de48f34396321400ee2771": "Knights Templar",
+    "56de49564396321400ee277a": "Africa",
+    "56de49a8cffd8e1900b4b7a7": "Bethencourt",
+    "56de49a8cffd8e1900b4b7a8": "Enrique P\u00e9rez de Guzm\u00e1n, 2nd Count de Niebla",
+    "56de49a8cffd8e1900b4b7a9": "Enrique P\u00e9rez de Guzm\u00e1n, 2nd Count de Niebla",
+    "56de4a474396321400ee2786": "Channel Islands",
+    "56de4a474396321400ee2787": "two",
+    "56de4a89cffd8e1900b4b7bd": "Romanesque",
+    "56de4a89cffd8e1900b4b7be": "rounded",
+    "56de4b074396321400ee2793": "Early Gothic",
+    "56de4b074396321400ee2794": "Early Gothic",
+    "56de4b074396321400ee2795": "Kingdom of Sicily",
+    "56de4b5c4396321400ee2799": "early 11th century",
+    "56de4b5c4396321400ee279a": "the dukes",
+    "56de4bb84396321400ee27a2": "16th century",
+    "56de4c324396321400ee27ab": "embroidery",
+    "56de4c324396321400ee27ac": "Bayeux Tapestry",
+    "56de4c324396321400ee27ad": "Odo, the Bishop of Bayeux and first Earl of Kent",
+    "56de51244396321400ee27ef": "sculptured fonts, capitals, and more importantly mosaics",
+    "56de51c64396321400ee27f7": "11th",
+    "56de51c64396321400ee27f8": "William of Volpiano and John of Ravenna",
+    "56de52614396321400ee27fb": "southern Italy",
+    "56de52614396321400ee27fc": "Sant'Eufemia",
+    "56de52614396321400ee27fd": "Robert Guiscard",
+    "56de52614396321400ee27fe": "singing",
+    "56df9e2838dc4217001520f6": "1856",
+    "56df9e2838dc4217001520f8": "Serbian American",
+    "56df9e2838dc4217001520f9": "1943",
+    "56e0b94b7aa994140058e6b7": "10 July 1856",
+    "56e0b94b7aa994140058e6b8": "1943",
+    "56e0b94b7aa994140058e6b9": "Serbian American",
+    "56e0b94b7aa994140058e6bb": "alternating current",
+    "56df9ee138dc421700152108": "1884",
+    "56df9ee138dc421700152109": "Thomas Edison",
+    "56df9ee138dc42170015210a": "George Westinghouse",
+    "56df9ee138dc42170015210b": "New York City",
+    "56df9ee138dc42170015210c": "War of Currents",
+    "56e0b9d57aa994140058e6c1": "1884",
+    "56e0b9d57aa994140058e6c2": "Thomas Edison",
+    "56e0b9d57aa994140058e6c3": "New York City",
+    "56e0b9d57aa994140058e6c4": "George Westinghouse",
+    "56e0b9d57aa994140058e6c5": "AC induction motor and transformer",
+    "56dfa01738dc42170015211d": "1893",
+    "56dfa01738dc42170015211e": "high-voltage",
+    "56dfa01738dc42170015211f": "mechanical oscillators/generators, electrical discharge tubes, and early X-ray imaging",
+    "56e0bab7231d4119001ac35b": "Colorado Springs",
+    "56e0bab7231d4119001ac35c": "1893",
+    "56e0bab7231d4119001ac35d": "wireless controlled boat",
+    "56e0bab7231d4119001ac35e": "Wardenclyffe Tower project",
+    "56dfa0d84a1a83140091ebb7": "1943",
+    "56dfa0d84a1a83140091ebb8": "the SI unit of magnetic flux density",
+    "56dfa0d84a1a83140091ebb9": "New York hotels",
+    "56dfa0d84a1a83140091ebba": "mad scientist",
+    "56e0bb9f7aa994140058e6cb": "patents earned him a considerable amount of money, much of which was used to finance his own projects with varying degrees of success",
+    "56e0bb9f7aa994140058e6cc": "1943",
+    "56e0bb9f7aa994140058e6cd": "the SI unit of magnetic flux density",
+    "56e0bb9f7aa994140058e6ce": "1990s",
+    "56e0bb9f7aa994140058e6cf": "showmanship",
+    "56dfa13d4a1a83140091ebc1": "Croatia",
+    "56dfa13d4a1a83140091ebc2": "Serbian Orthodox priest",
+    "56dfa13d4a1a83140091ebc3": "eidetic",
+    "56dfa13d4a1a83140091ebc4": "his mother's genetics and influence",
+    "56e0bcc0231d4119001ac36b": "Serbian Orthodox priest",
+    "56e0bcc0231d4119001ac36c": "Milutin Tesla",
+    "56e0bcc0231d4119001ac36d": "\u0110uka Tesla",
+    "56e0bcc0231d4119001ac36e": "making home craft tools, mechanical appliances, and the ability to memorize Serbian epic poems",
+    "56e0bcc0231d4119001ac36f": "western Serbia",
+    "56dfa1d34a1a83140091ebd4": "three",
+    "56dfa1d44a1a83140091ebd6": "German",
+    "56dfa1d44a1a83140091ebd7": "1862",
+    "56e0c0c7231d4119001ac375": "Dane",
+    "56e0c0c7231d4119001ac376": "Milka, Angelina and Marica",
+    "56e0c0c7231d4119001ac377": "killed in a horse-riding accident",
+    "56e0c0c7231d4119001ac378": "Gospi\u0107, Austrian Empire",
+    "56e0c0c7231d4119001ac379": "pastor",
+    "56dfa24a38dc421700152142": "Martin Sekuli\u0107",
+    "56dfa24a38dc421700152143": "German",
+    "56dfa24a38dc421700152144": "integral calculus",
+    "56dfa24a38dc421700152145": "cheating",
+    "56dfa24a38dc421700152146": "1873",
+    "56e0c1617aa994140058e6d5": "1870",
+    "56e0c1617aa994140058e6d6": "to attend school at the Higher Real Gymnasium",
+    "56e0c1617aa994140058e6d7": "Martin Sekuli\u0107",
+    "56e0c1617aa994140058e6d8": "German",
+    "56e0c1617aa994140058e6d9": "1873",
+    "56dfa2c54a1a83140091ebf2": "cholera",
+    "56dfa2c54a1a83140091ebf3": "nine months",
+    "56dfa2c54a1a83140091ebf4": "send him to the best engineering school",
+    "56dfa2c54a1a83140091ebf5": "enter the priesthood",
+    "56dfa2c54a1a83140091ebf6": "Smiljan",
+    "56e0c2307aa994140058e6df": "1873",
+    "56e0c2307aa994140058e6e0": "cholera",
+    "56e0c2307aa994140058e6e1": "nine months",
+    "56e0c2307aa994140058e6e2": "enter the priesthood",
+    "56e0c2307aa994140058e6e3": "send him to the best engineering school",
+    "56dfa3394a1a83140091ebfc": "Tomingaj",
+    "56dfa3394a1a83140091ebfd": "Mark Twain",
+    "56dfa3394a1a83140091ebfe": "the mountains",
+    "56dfa3394a1a83140091ebff": "1874",
+    "56dfa3394a1a83140091ec00": "hunter's garb",
+    "56e0c2bc231d4119001ac389": "being drafted into the Austro-Hungarian Army",
+    "56e0c2bc231d4119001ac38a": "1874",
+    "56e0c2bc231d4119001ac38b": "explored the mountains in hunter's garb. Tesla said that this contact with nature made him stronger, both physically and mentally. He read many books",
+    "56e0c2bc231d4119001ac38c": "Mark Twain",
+    "56dfa3c338dc421700152154": "1875",
+    "56dfa3c338dc421700152155": "Austrian Polytechnic",
+    "56dfa3c338dc421700152156": "1879",
+    "56dfa3c338dc421700152157": "gambling",
+    "56dfa3c338dc421700152158": "never graduated from the university",
+    "56e0cbf3231d4119001ac3ab": "Graz",
+    "56e0cbf3231d4119001ac3ac": "1875",
+    "56e0cbf3231d4119001ac3ad": "1879",
+    "56e0cbf3231d4119001ac3ae": "gambling",
+    "56e0cbf3231d4119001ac3af": "killed through overwork",
+    "56dfa7887aa994140058dfa9": "severed all relations with his family",
+    "56dfa7887aa994140058dfaa": "to hide the fact that he dropped out of school",
+    "56dfa7887aa994140058dfab": "draftsman",
+    "56dfa7887aa994140058dfac": "playing cards",
+    "56dfa7887aa994140058dfad": "nervous breakdown",
+    "56e0ccaa7aa994140058e717": "December 1878",
+    "56e0ccaa7aa994140058e718": "he dropped out of school",
+    "56e0ccaa7aa994140058e719": "drowned in the Mur River",
+    "56e0ccaa7aa994140058e71a": "draftsman",
+    "56e0ccaa7aa994140058e71b": "nervous breakdown",
+    "56dfaa047aa994140058dfbd": "not having a residence permit",
+    "56dfaa047aa994140058dfbe": "24 March 1879",
+    "56dfaa047aa994140058dfbf": "60",
+    "56dfaa047aa994140058dfc0": "a stroke",
+    "56dfaa047aa994140058dfc1": "taught a large class of students",
+    "56e0cd33231d4119001ac3bf": "not having a residence permit",
+    "56e0cd33231d4119001ac3c0": "24 March 1879",
+    "56e0cd33231d4119001ac3c1": "Higher Real Gymnasium",
+    "56e0cd33231d4119001ac3c2": "stroke",
+    "56dfac8e231d4119001abc5b": "Prague",
+    "56dfac8e231d4119001abc5c": "he was illiterate in Czech",
+    "56dfac8e231d4119001abc5e": "lectures",
+    "56dfac8e231d4119001abc5f": "Charles-Ferdinand University",
+    "56e0cdb9231d4119001ac3c7": "Prague",
+    "56e0cdb9231d4119001ac3c8": "January 1880",
+    "56e0cdb9231d4119001ac3c9": "Charles-Ferdinand University",
+    "56e0cdb9231d4119001ac3ca": "two of Tesla's uncles",
+    "56dfad0a7aa994140058dfc7": "Budapest",
+    "56dfad0a7aa994140058dfc8": "Budapest Telephone Exchange",
+    "56dfad0a7aa994140058dfc9": "chief electrician",
+    "56dfad0a7aa994140058dfca": "a telephone repeater or amplifier",
+    "56dfad0a7aa994140058dfcb": "chief electrician",
+    "56e0d402231d4119001ac3fc": "1881",
+    "56e0d402231d4119001ac3fd": "Budapest Telephone Exchange",
+    "56e0d402231d4119001ac3fe": "Budapest Telephone Exchange",
+    "56e0d402231d4119001ac3ff": "chief electrician",
+    "56dfb0c8231d4119001abc83": "1882",
+    "56dfb0c8231d4119001abc84": "France",
+    "56dfb0c8231d4119001abc85": "New York City",
+    "56dfb0c8231d4119001abc86": "Thomas Edison",
+    "56dfb0c8231d4119001abc87": "Edison Machine Works",
+    "56e0d54a7aa994140058e769": "Continental Edison Company",
+    "56e0d54a7aa994140058e76a": "France",
+    "56e0d54a7aa994140058e76b": "June 1884",
+    "56e0d54a7aa994140058e76c": "Thomas Edison",
+    "56e0d54a7aa994140058e76d": "Manhattan's lower east side",
+    "56dfb4be7aa994140058e00d": "fifty thousand dollars",
+    "56dfb4be7aa994140058e00e": "payment",
+    "56dfb4be7aa994140058e010": "months",
+    "56e0d6367aa994140058e773": "fifty thousand dollars",
+    "56e0d6367aa994140058e774": "American humor",
+    "56e0d6367aa994140058e775": "US$10 a week raise",
+    "56dfb5777aa994140058e021": "Robert Lane and Benjamin Vail",
+    "56dfb5777aa994140058e022": "Tesla Electric Light & Manufacturing",
+    "56dfb5777aa994140058e023": "installed electrical arc light based illumination systems",
+    "56dfb5777aa994140058e024": "patents",
+    "56dfb5777aa994140058e025": "electrical arc light based illumination systems designed by Tesla and also had designs for dynamo electric machine commutators",
+    "56e0d6cf231d4119001ac420": "Robert Lane and Benjamin Vail",
+    "56e0d6cf231d4119001ac421": "1886",
+    "56e0d6cf231d4119001ac422": "Tesla Electric Light & Manufacturing",
+    "56e0d6cf231d4119001ac423": "installed electrical arc light based illumination systems",
+    "56e0d6cf231d4119001ac424": "Robert Lane and Benjamin Vail",
+    "56dfb5f5231d4119001abcb5": "forced Tesla out leaving him penniless",
+    "56dfb5f5231d4119001abcb6": "penniless",
+    "56dfb5f5231d4119001abcb8": "work at various electrical repair jobs and even as a ditch digger",
+    "56dfb5f5231d4119001abcb9": "1886/1887",
+    "56e0d76d231d4119001ac42b": "assigned them to the company in lieu of stock",
+    "56e0d76d231d4119001ac42c": "ditch digger",
+    "56e0d76d231d4119001ac42d": "various electrical repair jobs",
+    "56dfb666231d4119001abcc0": "a Western Union superintendent",
+    "56dfb666231d4119001abcc1": "April 1887",
+    "56dfb666231d4119001abcc2": "\u2153 to Tesla, \u2153 to Peck and Brown, and \u2153 to fund development",
+    "56dfb666231d4119001abcc3": "89 Liberty Street in Manhattan",
+    "56e0d810231d4119001ac432": "late 1886",
+    "56e0d810231d4119001ac433": "Western Union superintendent",
+    "56e0d810231d4119001ac434": "Alfred S. Brown",
+    "56e0d810231d4119001ac435": "89 Liberty Street in Manhattan",
+    "56e0d810231d4119001ac436": "Tesla Electric Company",
+    "56dfb6d17aa994140058e053": "an induction motor",
+    "56dfb6d17aa994140058e054": "May 1888",
+    "56dfb6d17aa994140058e055": "commutator",
+    "56dfb6d17aa994140058e056": "sparking",
+    "56dfb6d17aa994140058e057": "simple self-starting",
+    "56e0d9e0231d4119001ac43c": "1887",
+    "56e0d9e0231d4119001ac43d": "advantages in long-distance, high-voltage transmission",
+    "56e0d9e0231d4119001ac43e": "commutator",
+    "56e0d9e0231d4119001ac43f": "May 1888",
+    "56e057e1231d4119001ac043": "a friend and publicist",
+    "56e057e1231d4119001ac044": "American Institute of Electrical Engineers",
+    "56e057e1231d4119001ac045": "1888",
+    "56e057e1231d4119001ac046": "Tesla's patent would probably control the market",
+    "56e057e1231d4119001ac047": "Thomas Commerford Martin",
+    "56e0dbb57aa994140058e779": "Thomas Commerford Martin",
+    "56e0dbb57aa994140058e77a": "George Westinghouse",
+    "56e0dbb57aa994140058e77b": "Galileo Ferraris",
+    "56e0dbb57aa994140058e77c": "physicist",
+    "56e0dbb57aa994140058e77d": "Westinghouse Electric & Manufacturing Company",
+    "56e05900231d4119001ac04d": "1888",
+    "56e05900231d4119001ac04e": "$60,000",
+    "56e05900231d4119001ac04f": "George Westinghouse",
+    "56e05900231d4119001ac051": "consultant",
+    "56e0dc667aa994140058e783": "$60,000",
+    "56e0dc667aa994140058e784": "July 1888",
+    "56e0dc667aa994140058e785": "$2,000",
+    "56e0dc667aa994140058e786": "Pittsburgh",
+    "56e059c8231d4119001ac057": "Pittsburgh",
+    "56e059c8231d4119001ac058": "alternating current system to power the city's streetcars",
+    "56e059c8231d4119001ac059": "60",
+    "56e059c8231d4119001ac05a": "DC traction motor",
+    "56e0e518231d4119001ac444": "power the city's streetcars",
+    "56e0e518231d4119001ac445": "DC traction motor",
+    "56e05a78231d4119001ac062": "Thomas Edison and George Westinghouse",
+    "56e05a78231d4119001ac063": "rival lighting systems",
+    "56e05a78231d4119001ac064": "AC development",
+    "56e05a78231d4119001ac065": "General Electric",
+    "56e0e5f37aa994140058e78b": "AC",
+    "56e0e5f37aa994140058e78c": "lighting systems",
+    "56e0e5f37aa994140058e78d": "1888",
+    "56e0e5f37aa994140058e78e": "the financial strain of buying up patents",
+    "56e0e5f37aa994140058e78f": "General Electric",
+    "56e07c86231d4119001ac1cd": "George Westinghouse",
+    "56e07c86231d4119001ac1ce": "Chicago",
+    "56e07c86231d4119001ac1cf": "General Electric bid",
+    "56e07c86231d4119001ac1d0": "Tesla Polyphase System",
+    "56e07c86231d4119001ac1d1": "Tesla Polyphase System",
+    "56e0ecd37aa994140058e7d5": "George Westinghouse",
+    "56e0ecd37aa994140058e7d6": "Chicago",
+    "56e0ecd37aa994140058e7d7": "1893",
+    "56e0ecd37aa994140058e7d8": "AC",
+    "56e0812c231d4119001ac213": "Richard Dean Adams",
+    "56e0812c231d4119001ac214": "Niagara Falls",
+    "56e0812c231d4119001ac215": "Westinghouse Electric",
+    "56e0812c231d4119001ac216": "General Electric",
+    "56e0812c231d4119001ac217": "two-phased system",
+    "56e0ee12231d4119001ac459": "Richard Dean Adams",
+    "56e0ee12231d4119001ac45a": "1893",
+    "56e0ee12231d4119001ac45b": "two-phased",
+    "56e0ee12231d4119001ac45c": "most reliable",
+    "56e089ab231d4119001ac285": "1896",
+    "56e089ab231d4119001ac287": "$216,000",
+    "56e089ab231d4119001ac288": "$2.50 per AC horsepower",
+    "56e089ab231d4119001ac289": "$200,000",
+    "56e0ef0c231d4119001ac462": "J. P. Morgan",
+    "56e0ef0c231d4119001ac463": "$200,000",
+    "56e0ef0c231d4119001ac464": "$216,000",
+    "56e08a1f7aa994140058e5da": "35",
+    "56e08a1f7aa994140058e5db": "South Fifth Avenue",
+    "56e08a1f7aa994140058e5dc": "electric lamps",
+    "56e08a1f7aa994140058e5dd": "Tesla coil",
+    "56e0ef90231d4119001ac468": "30 July 1891",
+    "56e0ef90231d4119001ac469": "Tesla coil",
+    "56e0ef90231d4119001ac46a": "35",
+    "56e0ef90231d4119001ac46b": "wireless",
+    "56e08aa0231d4119001ac299": "American Institute of Electrical Engineers",
+    "56e08aa0231d4119001ac29a": "American Institute of Electrical Engineers",
+    "56e08aa0231d4119001ac29d": "1894",
+    "56e0f019231d4119001ac470": "vice president",
+    "56e0f019231d4119001ac471": "1892 to 1894",
+    "56e0f019231d4119001ac472": "Institute of Radio Engineers",
+    "56e08b3c231d4119001ac2a4": "he had noticed damaged film in his laboratory in previous experiments",
+    "56e08b3c231d4119001ac2a5": "5th Avenue laboratory fire",
+    "56e08b3c231d4119001ac2a6": "December 1895",
+    "56e08b3c231d4119001ac2a7": "metal locking screw on the camera lens",
+    "56e0f32d231d4119001ac4c8": "March 1895",
+    "56e0f32d231d4119001ac4c9": "Roentgen rays",
+    "56e0f32d231d4119001ac4ca": "lost in the 5th Avenue laboratory fire of March 1895",
+    "56e0f32d231d4119001ac4cb": "X-ray",
+    "56e0f32d231d4119001ac4cc": "Mark Twain",
+    "56e08d32231d4119001ac2ad": "X-ray imaging",
+    "56e08d32231d4119001ac2ae": "March 1896",
+    "56e08d32231d4119001ac2af": "radiography",
+    "56e08d32231d4119001ac2b0": "X-rays",
+    "56e08d32231d4119001ac2b1": "Tesla Coil",
+    "56e0f5a6231d4119001ac4d4": "March 1896",
+    "56e0f5a6231d4119001ac4d5": "Tesla Coil",
+    "56e0f5a6231d4119001ac4d6": "radiography",
+    "56e08f4a231d4119001ac2b7": "longitudinal waves",
+    "56e08f4a231d4119001ac2b8": "various causes",
+    "56e08f4a231d4119001ac2b9": "ozone",
+    "56e08f4a231d4119001ac2ba": "his circuit and single-node X-ray-producing devices",
+    "56e08f4a231d4119001ac2bb": "force-free magnetic fields",
+    "56e0f6aa231d4119001ac4ee": "ozone generated in contact with the skin, and to a lesser extent, by nitrous acid",
+    "56e0f6aa231d4119001ac4ef": "longitudinal waves",
+    "56e0f6aa231d4119001ac4f0": "plasmas",
+    "56e0f6aa231d4119001ac4f1": "In his many notes",
+    "56e0e69b7aa994140058e795": "Benjamin Lamme",
+    "56e0e69b7aa994140058e796": "1893",
+    "56e0e69b7aa994140058e797": "Westinghouse Electric",
+    "56e0ed557aa994140058e7dd": "the Egg of Columbus",
+    "56e0ed557aa994140058e7e0": "Tesla",
+    "56e0fa5b7aa994140058e859": "11 July 1934",
+    "56e0fa5b7aa994140058e85a": "they will travel in concentrations",
+    "56e0fa5b7aa994140058e85b": "he could feel a sharp stinging pain where it entered his body",
+    "56e0fa5b7aa994140058e85c": "metal",
+    "56e0fb14231d4119001ac525": "National Electric Light Association",
+    "56e0fb14231d4119001ac526": "Tesla Coil",
+    "56e0fb14231d4119001ac527": "Franklin Institute",
+    "56e0fb887aa994140058e869": "1898",
+    "56e0fb887aa994140058e86a": "teleautomaton",
+    "56e0fb887aa994140058e86b": "Madison Square Garden",
+    "56e0fb887aa994140058e86c": "an electrical exhibition",
+    "56e0fb887aa994140058e86d": "monkey",
+    "56e0fc3f7aa994140058e877": "1900",
+    "56e0fc3f7aa994140058e878": "Guglielmo Marconi",
+    "56e0fc3f7aa994140058e879": "1901",
+    "56e0fc3f7aa994140058e87a": "1943",
+    "56e0fc3f7aa994140058e87b": "Supreme Court of the United States",
+    "56e0fd167aa994140058e881": "1899",
+    "56e0fd167aa994140058e882": "Colorado Springs",
+    "56e0fd167aa994140058e883": "15 June 1899",
+    "56e0fd167aa994140058e884": "five inches",
+    "56e0fde0cd28a01900c673ea": "atmospheric",
+    "56e0fde0cd28a01900c673eb": "stationary",
+    "56e0fde0cd28a01900c673ec": "a resonant frequency",
+    "56e0fe85e3433e1400422a6a": "lightning",
+    "56e0fe85e3433e1400422a6b": "up to 135 feet",
+    "56e0fe85e3433e1400422a6c": "15 miles away",
+    "56e0fe85e3433e1400422a6d": "Light bulbs within 100 feet of the lab glowed",
+    "56e0fe85e3433e1400422a6e": "swirling in circles with blue halos of St. Elmo's fire around their wings",
+    "56e0ff5dcd28a01900c673fb": "power outage",
+    "56e0ff5dcd28a01900c673fc": "burned out",
+    "56e0ff5dcd28a01900c673fd": "high frequency currents",
+    "56e0ff5dcd28a01900c673fe": "destroy",
+    "56e10043e3433e1400422a84": "another planet",
+    "56e10043e3433e1400422a85": "Mars",
+    "56e10043e3433e1400422a86": "Collier's Weekly",
+    "56e10043e3433e1400422a87": "communications from another planet",
+    "56e10043e3433e1400422a88": "July 1899",
+    "56e100b6cd28a01900c67403": "$100,000",
+    "56e100b6cd28a01900c67404": "Colorado Springs experiments",
+    "56e100b6cd28a01900c67405": "Colorado Springs experiments",
+    "56e100b6cd28a01900c67406": "1899",
+    "56e1011ecd28a01900c6740b": "7 January 1900",
+    "56e1011ecd28a01900c6740c": "torn down",
+    "56e1011ecd28a01900c6740d": "1904",
+    "56e1011ecd28a01900c6740e": "sold",
+    "56e10179cd28a01900c67413": "trans-Atlantic wireless telecommunications facility",
+    "56e10179cd28a01900c67414": "trans-Atlantic wireless telecommunications facility",
+    "56e10179cd28a01900c67415": "Shoreham, Long Island",
+    "56e101f4e3433e1400422a8e": "Morgan",
+    "56e101f4e3433e1400422a8f": "Panic of 1901",
+    "56e101f4e3433e1400422a91": "shocked",
+    "56e10296cd28a01900c67423": "over 50",
+    "56e10296cd28a01900c67424": "to complete the construction of Wardenclyffe.",
+    "56e10296cd28a01900c67425": "In December 1901, Marconi successfully transmitted the letter S from England to Newfoundland",
+    "56e10296cd28a01900c67426": "187 feet",
+    "56e10325cd28a01900c67435": "200",
+    "56e10325cd28a01900c67436": "16,000 rpm",
+    "56e10325cd28a01900c67437": "1906",
+    "56e10325cd28a01900c67438": "100\u20135,000 hp",
+    "56e10412e3433e1400422aba": "steam",
+    "56e10412e3433e1400422abb": "Houston Street lab",
+    "56e10412e3433e1400422abc": "the machine oscillated at the resonance frequency of his own building and, belatedly realizing the danger",
+    "56e10412e3433e1400422abd": "World Today",
+    "56e10412e3433e1400422abe": "set the earth's crust into such a state of vibration that it would rise and fall hundreds of feet and practically destroy civilization",
+    "56e1066be3433e1400422ae4": "electricity",
+    "56e1066be3433e1400422ae5": "by saturating them unconsciously with electricity",
+    "56e1066be3433e1400422ae6": "William H. Maxwell",
+    "56e1066be3433e1400422ae7": "superintendent of New York City schools",
+    "56e1075ae3433e1400422afa": "overseas",
+    "56e1075ae3433e1400422afb": "lost",
+    "56e1075ae3433e1400422afc": "sold",
+    "56e1075ae3433e1400422afd": "$20,000",
+    "56e1075ae3433e1400422afe": "Edison Medal",
+    "56e108abe3433e1400422b0c": "Electrical Experimenter",
+    "56e108abe3433e1400422b0d": "on a fluorescent screen",
+    "56e108abe3433e1400422b0e": "radar",
+    "56e108abe3433e1400422b0f": "\u00c9mile Girardeau",
+    "56e1097dcd28a01900c6748f": "Thomas Edison and Nikola Tesla",
+    "56e1097dcd28a01900c67490": "Thomas Edison and Nikola Tesla",
+    "56e1097dcd28a01900c67491": "because he has made known his intention to refuse the reward",
+    "56e1097dcd28a01900c67492": "announced a winner",
+    "56e10a28cd28a01900c674b1": "their animosity toward each other",
+    "56e10aa5cd28a01900c674b3": "38",
+    "56e10aa5cd28a01900c674b4": "Edison",
+    "56e10aa5cd28a01900c674b5": "1937",
+    "56e10b6ee3433e1400422b24": "1,655,114",
+    "56e10b6ee3433e1400422b25": "a biplane",
+    "56e10b6ee3433e1400422b26": "less than $1,000",
+    "56e10b6ee3433e1400422b27": "turbine",
+    "56e10c61e3433e1400422b36": "$125 per month",
+    "56e10c61e3433e1400422b37": "consulting fee\" to get around Tesla's aversion to accept charity",
+    "56e10c61e3433e1400422b38": "the rest of Tesla's life",
+    "56e10c61e3433e1400422b39": "bad publicity",
+    "56e10d2dcd28a01900c674d7": "mechanical energy",
+    "56e10d2dcd28a01900c674d8": "over any terrestrial distance",
+    "56e10d2dcd28a01900c674d9": "minimal",
+    "56e10d2dcd28a01900c674da": "mineral deposits",
+    "56e10d2dcd28a01900c674db": "1935",
+    "56e10ddce3433e1400422b48": "refused medical aid",
+    "56e10ddce3433e1400422b49": "a doctor",
+    "56e10ddce3433e1400422b4a": "three of his ribs were broken",
+    "56e10ddce3433e1400422b4b": "early 1938",
+    "56e10ddce3433e1400422b4c": "fall of 1937",
+    "56e10e73cd28a01900c674eb": "teleforce",
+    "56e10e73cd28a01900c674ec": "Van de Graaff generator",
+    "56e10e73cd28a01900c674ed": "infantry",
+    "56e10e73cd28a01900c674ee": "anti-aircraft purposes",
+    "56e10e73cd28a01900c674ef": "death ray",
+    "56e10f14e3433e1400422b5c": "1937",
+    "56e10f14e3433e1400422b5d": "luncheon",
+    "56e10f14e3433e1400422b5e": "tungsten",
+    "56e10f14e3433e1400422b5f": "high voltage",
+    "56e10f14e3433e1400422b60": "a little",
+    "56e10fbacd28a01900c67509": "charged particle beam weapons",
+    "56e10fbacd28a01900c6750a": "Nikola Tesla Museum archive in Belgrade",
+    "56e10fbacd28a01900c6750b": "Belgrade",
+    "56e10fbacd28a01900c6750c": "millions",
+    "56e10fbacd28a01900c6750d": "all war",
+    "56e11044e3433e1400422b76": "steal",
+    "56e11044e3433e1400422b77": "room",
+    "56e11044e3433e1400422b78": "papers",
+    "56e110c3cd28a01900c67531": "86",
+    "56e110c3cd28a01900c67532": "7 January 1943",
+    "56e110c3cd28a01900c67533": "maid Alice Monaghan",
+    "56e110c3cd28a01900c67534": "the \"do not disturb\" sign",
+    "56e110c3cd28a01900c67535": "coronary thrombosis",
+    "56e11161e3433e1400422b86": "transported to the Manhattan Storage and Warehouse Company under the Office of Alien Property (OAP) seal",
+    "56e11161e3433e1400422b87": "John G. Trump",
+    "56e11161e3433e1400422b89": "nothing",
+    "56e11161e3433e1400422b8a": "Manhattan Storage and Warehouse Company",
+    "56e111e5e3433e1400422b90": "New York City mayor Fiorello La Guardia",
+    "56e111e5e3433e1400422b91": "Louis Adamic",
+    "56e111e5e3433e1400422b92": "12 January",
+    "56e111e5e3433e1400422b93": "two thousand",
+    "56e111e5e3433e1400422b94": "Ferncliff Cemetery in Ardsley, New York",
+    "56e1127bcd28a01900c67549": "Belgrade",
+    "56e1127bcd28a01900c6754a": "Sava Kosanovi\u0107",
+    "56e1127bcd28a01900c6754b": "Charlotte Muzar",
+    "56e1127bcd28a01900c6754c": "United States",
+    "56e1127bcd28a01900c6754d": "1957",
+    "56e11996e3433e1400422bde": "278",
+    "56e11996e3433e1400422bdf": "26",
+    "56e11996e3433e1400422be0": "Canada",
+    "56e11996e3433e1400422be1": "patent archives",
+    "56e11a16cd28a01900c675b5": "8:10 p.m",
+    "56e11a16cd28a01900c675b6": "8:10 p.m.",
+    "56e11a16cd28a01900c675b7": "6:00 p.m",
+    "56e11a16cd28a01900c675b8": "the headwaiter",
+    "56e11a73e3433e1400422bf0": "8 to 10 miles",
+    "56e11a73e3433e1400422bf1": "it stimulated his brain cells",
+    "56e11a73e3433e1400422bf2": "squished his toes",
+    "56e11a73e3433e1400422bf3": "his brain cells",
+    "56e11afbcd28a01900c675c7": "telepathy",
+    "56e11afbcd28a01900c675c8": "newspaper editor",
+    "56e11afbcd28a01900c675c9": "one",
+    "56e11ba9cd28a01900c675d7": "pigeons",
+    "56e11ba9cd28a01900c675d8": "over $2,000",
+    "56e11ba9cd28a01900c675d9": "injured white pigeon",
+    "56e11ba9cd28a01900c675da": "the park",
+    "56e11ba9cd28a01900c675db": "hotel room",
+    "56e11c24e3433e1400422c18": "142 pounds",
+    "56e11c24e3433e1400422c19": "6 feet 2 inches",
+    "56e11c24e3433e1400422c1a": "1888",
+    "56e11c24e3433e1400422c1b": "New York City",
+    "56e11cedcd28a01900c675e7": "eight",
+    "56e11cedcd28a01900c675e8": "blinding flashes of light would appear before his eyes, often accompanied by visions",
+    "56e11cedcd28a01900c675e9": "picture thinking",
+    "56e11cedcd28a01900c675ea": "blinding flashes of light would appear before his eyes",
+    "56e11cedcd28a01900c675eb": "photographic",
+    "56e11d8ecd28a01900c675f1": "more than 48 hours",
+    "56e11d8ecd28a01900c675f2": "84 hours",
+    "56e11d8ecd28a01900c675f3": "Graz",
+    "56e11d8ecd28a01900c675f4": "Kenneth Swezey",
+    "56e11d8ecd28a01900c675f5": "journalist",
+    "56e11e57e3433e1400422c28": "chastity",
+    "56e11e57e3433e1400422c29": "women",
+    "56e11e57e3433e1400422c2a": "toward the end of his life",
+    "56e11f05e3433e1400422c2e": "Dorothy Skerrit",
+    "56e11f05e3433e1400422c2f": "Robert Underwood Johnson",
+    "56e11f05e3433e1400422c30": "seclude himself",
+    "56e11f05e3433e1400422c31": "asocial",
+    "56e11f05e3433e1400422c32": "friend",
+    "56e11f83cd28a01900c67611": "Francis Marion Crawford",
+    "56e11f83cd28a01900c67612": "his lab",
+    "56e11f83cd28a01900c67613": "late 1920s",
+    "56e12005cd28a01900c67617": "overweight people",
+    "56e12005cd28a01900c67618": "a secretary",
+    "56e12005cd28a01900c67619": "her weight",
+    "56e12005cd28a01900c6761a": "change her dress",
+    "56e120a1e3433e1400422c38": "electron",
+    "56e120a1e3433e1400422c39": "ether",
+    "56e120a1e3433e1400422c3a": "transmitted electrical energy",
+    "56e120a1e3433e1400422c3b": "19th",
+    "56e12110e3433e1400422c4a": "Einstein",
+    "56e12110e3433e1400422c4b": "antagonistic",
+    "56e12110e3433e1400422c4c": "relativity",
+    "56e121b7e3433e1400422c50": "dynamic theory of gravity",
+    "56e121b7e3433e1400422c51": "1892",
+    "56e121b7e3433e1400422c52": "curved",
+    "56e121b7e3433e1400422c53": "81",
+    "56e122dacd28a01900c67639": "selective breeding version of eugenics",
+    "56e122dacd28a01900c6763a": "ruthless workings of nature",
+    "56e122dacd28a01900c6763b": "pity",
+    "56e122dacd28a01900c6763c": "1937",
+    "56e1239acd28a01900c67641": "Queen Bees",
+    "56e1239acd28a01900c67642": "1926",
+    "56e1239acd28a01900c67643": "Queen Bees",
+    "56e12477e3433e1400422c5e": "post-World War I",
+    "56e12477e3433e1400422c5f": "Science and Discovery",
+    "56e12477e3433e1400422c60": "20 December 1914",
+    "56e12477e3433e1400422c61": "the League of Nations",
+    "56e124f1cd28a01900c6764f": "Orthodox Christian",
+    "56e124f1cd28a01900c67650": "fanaticism",
+    "56e124f1cd28a01900c67651": "Buddhism and Christianity",
+    "56e1254ae3433e1400422c66": "A Machine to End War",
+    "56e1254ae3433e1400422c67": "uncertain",
+    "56e1254ae3433e1400422c68": "War",
+    "56e125b6e3433e1400422c6c": "The Tesla Papers",
+    "56e125b6e3433e1400422c6d": "magazines and journals",
+    "56e125b6e3433e1400422c6e": "Ben Johnston",
+    "56e1262fcd28a01900c67655": "the web",
+    "56e1262fcd28a01900c67656": "1900",
+    "56e1262fcd28a01900c67657": "Inventions, Researches and Writings of Nikola Tesla",
+    "56e126dae3433e1400422c7c": "science fiction",
+    "56e126dae3433e1400422c7d": "science fiction",
+    "56e126dae3433e1400422c7e": "several",
+    "56e127bccd28a01900c6765b": "Time magazine",
+    "56e127bccd28a01900c6765c": "75th birthday",
+    "56e127bccd28a01900c6765d": "electrical power generation",
+    "56e127bccd28a01900c6765e": "Albert Einstein",
+    "56e127bccd28a01900c6765f": "more than 70",
+    "56e16182e3433e1400422e28": "Computational complexity theory",
+    "56e16182e3433e1400422e29": "inherent difficulty",
+    "56e16182e3433e1400422e2a": "A computational problem",
+    "56e16839cd28a01900c67887": "significant resources",
+    "56e16839cd28a01900c67888": "mathematical models of computation",
+    "56e16839cd28a01900c67889": "time and storage",
+    "56e16839cd28a01900c6788a": "the number of gates",
+    "56e16839cd28a01900c6788b": "to determine the practical limits on what computers can and cannot do",
+    "56e17644e3433e1400422f40": "analysis of algorithms and computability theory",
+    "56e17644e3433e1400422f41": "computational complexity theory",
+    "56e17644e3433e1400422f42": "computational complexity theory",
+    "56e17644e3433e1400422f43": "theoretical",
+    "56e17a7ccd28a01900c679a1": "a problem instance",
+    "56e17a7ccd28a01900c679a2": "a problem",
+    "56e17a7ccd28a01900c679a3": "concrete",
+    "56e17a7ccd28a01900c679a4": "the solution",
+    "56e17a7ccd28a01900c679a5": "the solution",
+    "56e17e6ee3433e1400422f7f": "2000",
+    "56e17e6ee3433e1400422f80": "a round trip through all sites in Milan",
+    "56e17e6ee3433e1400422f81": "computational problems and not particular problem instances",
+    "56e181d9e3433e1400422fa0": "a problem instance",
+    "56e181d9e3433e1400422fa1": "binary alphabet",
+    "56e181d9e3433e1400422fa2": "bitstrings",
+    "56e181d9e3433e1400422fa3": "binary notation",
+    "56e181d9e3433e1400422fa4": "adjacency matrices",
+    "56e190bce3433e1400422fc8": "Decision problems",
+    "56e190bce3433e1400422fc9": "yes or no, or alternately either 1 or 0",
+    "56e190bce3433e1400422fca": "yes or no, or alternately either 1 or 0",
+    "56e190bce3433e1400422fcb": "yes",
+    "56e190bce3433e1400422fcc": "yes",
+    "56e19557e3433e1400422fee": "arbitrary",
+    "56e19557e3433e1400422ff0": "The formal language",
+    "56e19557e3433e1400422ff1": "binary strings",
+    "56e19724cd28a01900c679f6": "computational problem",
+    "56e19724cd28a01900c679f7": "single",
+    "56e19724cd28a01900c679f8": "function problem",
+    "56e19724cd28a01900c679f9": "integer factorization problem",
+    "56e19724cd28a01900c679fa": "more complex",
+    "56e1a0dccd28a01900c67a2e": "decision problems",
+    "56e1a0dccd28a01900c67a2f": "the set of triples",
+    "56e1a38de3433e140042305c": "the size of the input in bits",
+    "56e1a38de3433e140042305d": "instance",
+    "56e1a38de3433e140042305e": "as a function of the size of the instance",
+    "56e1a38de3433e140042305f": "bits",
+    "56e1a38de3433e1400423060": "input size",
+    "56e1a564cd28a01900c67a48": "Cobham",
+    "56e1a564cd28a01900c67a49": "the time taken",
+    "56e1a564cd28a01900c67a4a": "worst-case time complexity T(n)",
+    "56e1a564cd28a01900c67a4b": "the maximum time taken over all inputs of size n",
+    "56e1a564cd28a01900c67a4c": "polynomial time algorithm",
+    "56e1aba0e3433e1400423094": "A Turing machine",
+    "56e1aba0e3433e1400423095": "an algorithm",
+    "56e1aba0e3433e1400423097": "Turing machine",
+    "56e1aba0e3433e1400423098": "symbols",
+    "56e1aff7cd28a01900c67a68": "deterministic Turing machine",
+    "56e1aff7cd28a01900c67a69": "rules",
+    "56e1aff7cd28a01900c67a6a": "probabilistic Turing machine",
+    "56e1aff7cd28a01900c67a6b": "non-deterministic",
+    "56e1aff7cd28a01900c67a6c": "randomized algorithms",
+    "56e1b00ce3433e140042309e": "complexity classes",
+    "56e1b00ce3433e140042309f": "when resources (such as time or space) are bounded",
+    "56e1b00ce3433e14004230a1": "alternating Turing machines",
+    "56e1b169cd28a01900c67a72": "random access machines",
+    "56e1b169cd28a01900c67a73": "time and memory consumption",
+    "56e1b169cd28a01900c67a74": "time and memory",
+    "56e1b169cd28a01900c67a75": "the machines operate deterministically",
+    "56e1b355e3433e14004230b0": "non-deterministic",
+    "56e1b355e3433e14004230b1": "more unusual resources",
+    "56e1b355e3433e14004230b2": "mathematical models we want to analyze",
+    "56e1b355e3433e14004230b3": "non-deterministic time",
+    "56e1b62ecd28a01900c67aa3": "\"yes\" or \"no\"",
+    "56e1b62ecd28a01900c67aa4": "difficulty",
+    "56e1b62ecd28a01900c67aa5": "DTIME(f(n))",
+    "56e1b62ecd28a01900c67aa6": "time and space",
+    "56e1b754cd28a01900c67abc": "complexity",
+    "56e1b754cd28a01900c67abd": "computational",
+    "56e1b754cd28a01900c67abe": "Blum complexity axioms",
+    "56e1b754cd28a01900c67abf": "complexity measures",
+    "56e1b754cd28a01900c67ac0": "complexity",
+    "56e1b8f3e3433e14004230e6": "best, worst and average",
+    "56e1b8f3e3433e14004230e7": "time complexity",
+    "56e1b8f3e3433e14004230e8": "time complexity (or any other complexity measure) of different inputs of the same size",
+    "56e1b8f3e3433e14004230e9": "time complexity (or any other complexity measure) of different inputs",
+    "56e1ba41cd28a01900c67ae0": "quicksort",
+    "56e1ba41cd28a01900c67ae1": "O(n2)",
+    "56e1ba41cd28a01900c67ae2": "O(n2)",
+    "56e1bc3ae3433e1400423104": "the most efficient algorithm solving a given problem",
+    "56e1bc3ae3433e1400423105": "analysis of algorithms",
+    "56e1bc3ae3433e1400423106": "lower bounds",
+    "56e1bc3ae3433e1400423107": "upper bound",
+    "56e1bc3ae3433e1400423108": "all possible algorithms\"",
+    "56e1bd4acd28a01900c67afc": "big O notation",
+    "56e1bd4acd28a01900c67afd": "constant factors and smaller terms",
+    "56e1bd4acd28a01900c67afe": "in big O notation one would write T(n) = O(n2)",
+    "56e1bd4acd28a01900c67aff": "the specific details of the computational model used",
+    "56e1c0f6cd28a01900c67b2c": "complexity classes",
+    "56e1c0f6cd28a01900c67b2d": "framework",
+    "56e1c0f6cd28a01900c67b2e": "complicated definitions",
+    "56e1c2eee3433e1400423134": "the chosen machine model",
+    "56e1c2eee3433e1400423135": "quadratic time",
+    "56e1c2eee3433e1400423136": "single-tape",
+    "56e1c2eee3433e1400423137": "Cobham-Edmonds",
+    "56e1c2eee3433e1400423138": "complexity class P",
+    "56e1c3e1e3433e1400423148": "time or space",
+    "56e1c3e1e3433e1400423149": "bounding",
+    "56e1c3e1e3433e140042314a": "complexity classes",
+    "56e1c4fce3433e140042314e": "BPP, ZPP and RP",
+    "56e1c4fce3433e140042314f": "Boolean",
+    "56e1c4fce3433e1400423150": "quantum",
+    "56e1c4fce3433e1400423151": "#P",
+    "56e1c4fce3433e1400423152": "Interactive",
+    "56e1c720e3433e140042316a": "computation time",
+    "56e1c720e3433e140042316b": "DTIME(n2",
+    "56e1c720e3433e140042316c": "space hierarchy theorems",
+    "56e1c720e3433e140042316d": "a proper hierarchy",
+    "56e1c720e3433e140042316e": "quantitative",
+    "56e1c7e2cd28a01900c67b74": "The time and space hierarchy theorems",
+    "56e1c7e2cd28a01900c67b75": "strictly contained in EXPTIME",
+    "56e1c7e2cd28a01900c67b76": "PSPACE",
+    "56e1c9bfe3433e1400423192": "a reduction",
+    "56e1c9bfe3433e1400423193": "another problem",
+    "56e1c9bfe3433e1400423194": "no more difficult",
+    "56e1c9bfe3433e1400423195": "Cook reductions, Karp reductions and Levin reductions",
+    "56e1c9bfe3433e1400423196": "log-space reductions",
+    "56e1cbe2cd28a01900c67bac": "polynomial-time reduction",
+    "56e1cbe2cd28a01900c67bad": "polynomial time",
+    "56e1cbe2cd28a01900c67bae": "polynomial time",
+    "56e1cbe2cd28a01900c67baf": "input",
+    "56e1cbe2cd28a01900c67bb0": "multiplication",
+    "56e1ce08e3433e14004231a4": "the type of reduction being used",
+    "56e1ce08e3433e14004231a5": "an algorithm for X allows us to solve any problem in C. Of course, the notion of hard problems depends on the type of reduction being used",
+    "56e1ce08e3433e14004231a6": "solve any problem in C",
+    "56e1ce08e3433e14004231a8": "polynomial-time reductions are commonly used. In particular, the set of problems that are hard for NP is the set of NP-hard problems",
+    "56e1d9fee3433e14004231cb": "NP-complete",
+    "56e1d9fee3433e14004231cc": "most difficult",
+    "56e1d9fee3433e14004231cd": "no known polynomial-time solution",
+    "56e1d9fee3433e14004231ce": "the set, finding an NP-complete problem that can be solved in polynomial time would mean that P = NP",
+    "56e1dc62cd28a01900c67bca": "P",
+    "56e1dc62cd28a01900c67bcb": "Cobham\u2013Edmonds thesis",
+    "56e1dc62cd28a01900c67bcc": "P",
+    "56e1dc62cd28a01900c67bcd": "Boolean satisfiability problem, the Hamiltonian path problem and the vertex cover problem",
+    "56e1dc62cd28a01900c67bce": "deterministic Turing machines",
+    "56e1ddfce3433e14004231d5": "more efficient solutions",
+    "56e1ddfce3433e14004231d6": "protein structure prediction",
+    "56e1ddfce3433e14004231d8": "US$1,000,000",
+    "56e1ded7cd28a01900c67bd4": "Ladner",
+    "56e1ded7cd28a01900c67bd5": "NP-intermediate problems",
+    "56e1ded7cd28a01900c67bd6": "The graph isomorphism problem, the discrete logarithm problem and the integer factorization problem",
+    "56e1e9dfe3433e14004231fc": "graph isomorphism",
+    "56e1e9dfe3433e14004231fd": "NP-complete",
+    "56e1e9dfe3433e14004231fe": "P",
+    "56e1e9dfe3433e14004231ff": "second",
+    "56e1e9dfe3433e1400423200": "Laszlo Babai and Eugene Luks",
+    "56e1ec83cd28a01900c67c0a": "integer factorization problem",
+    "56e1ec83cd28a01900c67c0b": "k",
+    "56e1ec83cd28a01900c67c0c": "RSA algorithm",
+    "56e1ec83cd28a01900c67c0e": "general number field sieve",
+    "56e1ee4de3433e1400423210": "all these complexity classes collapse to one class",
+    "56e1ee4de3433e1400423211": "unequal",
+    "56e1ee4de3433e1400423212": "P and PSPACE",
+    "56e1ee4de3433e1400423214": "Proving that any of these classes are unequal",
+    "56e1efa0e3433e140042321a": "co-NP",
+    "56e1efa0e3433e140042321b": "reversed",
+    "56e1efa0e3433e140042321c": "NP is not equal",
+    "56e1efa0e3433e140042321d": "if these two complexity classes are not equal",
+    "56e1f10ee3433e1400423222": "L",
+    "56e1f10ee3433e1400423223": "the set of all problems that can be solved in logarithmic space) is strictly contained in P or equal",
+    "56e1f10ee3433e1400423224": "complexity classes",
+    "56e1f10ee3433e1400423225": "NL and NC",
+    "56e1f10ee3433e1400423226": "distinct or equal classes",
+    "56e1fc57e3433e140042322c": "intractable problems",
+    "56e1fc57e3433e140042322f": "complexity theory",
+    "56e1fc57e3433e1400423230": "NP-complete problems",
+    "56e1febfe3433e1400423236": "Presburger",
+    "56e1febfe3433e1400423237": "algorithms",
+    "56e1febfe3433e1400423238": "NP-complete knapsack",
+    "56e1febfe3433e1400423239": "less than quadratic time",
+    "56e1febfe3433e140042323a": "NP-complete Boolean satisfiability problem",
+    "56e200e4cd28a01900c67c14": "Turing machines",
+    "56e200e4cd28a01900c67c15": "Alan Turing",
+    "56e200e4cd28a01900c67c16": "Turing machines",
+    "56e200e4cd28a01900c67c17": "1936",
+    "56e200e4cd28a01900c67c18": "a very robust and flexible simplification of a computer",
+    "56e202e9e3433e1400423240": "\"On the Computational Complexity of Algorithms",
+    "56e202e9e3433e1400423241": "Juris Hartmanis and Richard Stearns",
+    "56e202e9e3433e1400423242": "1965",
+    "56e202e9e3433e1400423243": "time and space complexity",
+    "56e202e9e3433e1400423244": "1965",
+    "56e2042ecd28a01900c67c1e": "John Myhill",
+    "56e2042ecd28a01900c67c1f": "1961",
+    "56e2042ecd28a01900c67c20": "Hisao Yamada",
+    "56e20a27cd28a01900c67c24": "input encoding",
+    "56e20a27cd28a01900c67c25": "input encoding, one tries to keep the discussion abstract enough to be independent of the choice of encoding",
+    "56e20a3ae3433e140042324a": "Manuel Blum",
+    "56e20a3ae3433e140042324b": "speed-up theorem",
+    "56e20a3ae3433e140042324d": "Reducibility Among Combinatorial Problems",
+    "56e20a3ae3433e140042324e": "21",
+    "56e7477700c9c71400d76f23": "the curriculum",
+    "56e7477700c9c71400d76f24": "pedagogy",
+    "56e7477700c9c71400d76f25": "university or college",
+    "56e7477700c9c71400d76f26": "a lesson plan",
+    "56e7477700c9c71400d76f27": "a school or other place of formal education",
+    "56e748a200c9c71400d76f37": "cultures",
+    "56e748a200c9c71400d76f38": "numeracy",
+    "56e748a200c9c71400d76f39": "craftsmanship",
+    "56e748a200c9c71400d76f3a": "literacy",
+    "56e749dd00c9c71400d76f51": "a family member",
+    "56e749dd00c9c71400d76f52": "home schooling",
+    "56e749dd00c9c71400d76f53": "formal",
+    "56e749dd00c9c71400d76f54": "family member",
+    "56e749dd00c9c71400d76f55": "knowledge or skills",
+    "56e74af500c9c71400d76f65": "gurus, mullahs, rabbis, pastors/youth pastors and lamas",
+    "56e74af500c9c71400d76f66": "religious",
+    "56e74af500c9c71400d76f67": "Quran, Torah or Bible",
+    "56e74af500c9c71400d76f68": "Religious and spiritual teachers",
+    "56e74bf937bdd419002c3e33": "homeschooling",
+    "56e74bf937bdd419002c3e34": "paid professionals",
+    "56e74bf937bdd419002c3e35": "Chartered",
+    "56e74bf937bdd419002c3e36": "homeschooling, or in the wider community",
+    "56e74bf937bdd419002c3e37": "paid professionals",
+    "56e74d1f00c9c71400d76f6e": "school functions",
+    "56e74d1f00c9c71400d76f6f": "extracurricular",
+    "56e74d1f00c9c71400d76f70": "extracurricular activities",
+    "56e74d1f00c9c71400d76f71": "teachers",
+    "56e74e4800c9c71400d76f76": "teacher's colleges",
+    "56e74e4800c9c71400d76f77": "to serve and protect the public interest",
+    "56e74e4800c9c71400d76f78": "the public interest",
+    "56e74e4800c9c71400d76f79": "public interest through certifying, governing and enforcing the standards of practice for the teaching profession",
+    "56e74e4800c9c71400d76f7a": "standards of practice",
+    "56e74faf00c9c71400d76f94": "complaints involving members",
+    "56e74faf00c9c71400d76f95": "complaints involving members",
+    "56e74faf00c9c71400d76f96": "the college",
+    "56e74faf00c9c71400d76f97": "teacher's colleges",
+    "56e74faf00c9c71400d76f98": "Teaching Unions",
+    "56e7504437bdd419002c3e5b": "outdoors",
+    "56e7504437bdd419002c3e5c": "tutor",
+    "56e7504437bdd419002c3e5d": "outdoors",
+    "56e7504437bdd419002c3e5e": "facilitate student learning",
+    "56e751fb00c9c71400d76fa8": "a course of study and lesson plan",
+    "56e751fb00c9c71400d76fa9": "pedagogy",
+    "56e751fb00c9c71400d76faa": "pedagogy",
+    "56e751fb00c9c71400d76fab": "increasing use of technology, specifically the rise of the internet",
+    "56e751fb00c9c71400d76fac": "the internet",
+    "56e7535037bdd419002c3e6f": "a course of study, lesson plan, or a practical skill",
+    "56e7535037bdd419002c3e70": "the relevant authority",
+    "56e7535037bdd419002c3e71": "learning disabilities",
+    "56e7535037bdd419002c3e72": "infants to adults",
+    "56e7535037bdd419002c3e73": "standardized",
+    "56e7542f00c9c71400d76fbc": "particular skills",
+    "56e7542f00c9c71400d76fbd": "self-study and problem solving",
+    "56e7542f00c9c71400d76fbe": "encourage",
+    "56e7542f00c9c71400d76fbf": "inspire the bored, deflate",
+    "56e7542f00c9c71400d76fc0": "coach using the whole gamut of psychology to get each new class of rookies off the bench and into the game",
+    "56e7550700c9c71400d76fc6": "the relationship between teachers and children",
+    "56e7550700c9c71400d76fc7": "the whole curriculum",
+    "56e7550700c9c71400d76fc8": "different subject specialists",
+    "56e7550700c9c71400d76fc9": "primary school",
+    "56e7550700c9c71400d76fca": "form tutor, specialist teacher and surrogate parent",
+    "56e7560937bdd419002c3e8b": "alternative",
+    "56e7560937bdd419002c3e8c": "platoon\" system",
+    "56e7560937bdd419002c3e8d": "staying with the same group of peers",
+    "56e7560937bdd419002c3e8e": "students learn from teachers who specialize in one subject and who tend to be more knowledgeable in that one area than a teacher who teaches many subjects",
+    "56e7560937bdd419002c3e8f": "United States",
+    "56e756bc37bdd419002c3e95": "Co-teaching",
+    "56e756bc37bdd419002c3e96": "two or more",
+    "56e756bc37bdd419002c3e97": "learning",
+    "56e756bc37bdd419002c3e98": "in sync",
+    "56e756bc37bdd419002c3e99": "social networking",
+    "56e7578a37bdd419002c3ea9": "corporal punishment",
+    "56e7578a37bdd419002c3eaa": "substitute parent",
+    "56e7578a37bdd419002c3eab": "parental discipline",
+    "56e7578a37bdd419002c3eac": "most common form of school discipline",
+    "56e7578a37bdd419002c3ead": "While a child was in school",
+    "56e7586d37bdd419002c3eb3": "one of the most common",
+    "56e7586d37bdd419002c3eb4": "throughout much of the world",
+    "56e7586d37bdd419002c3eb5": "United States",
+    "56e7586d37bdd419002c3eb6": "US Supreme Court",
+    "56e7586d37bdd419002c3eb7": "physical pain",
+    "56e7591b00c9c71400d76fec": "30",
+    "56e7591b00c9c71400d76fed": "in the classroom or hallway, but nowadays the punishment is usually given privately in the principal's office",
+    "56e7591b00c9c71400d76fee": "declining",
+    "56e7591b00c9c71400d76fef": "wooden paddle",
+    "56e7591b00c9c71400d76ff0": "in the classroom or hallway, but nowadays the punishment is usually given privately in the principal's office",
+    "56e759bb00c9c71400d77000": "caning",
+    "56e759bb00c9c71400d77001": "Asian, African and Caribbean",
+    "56e759bb00c9c71400d77002": "School corporal punishment",
+    "56e75a9037bdd419002c3ec7": "detention",
+    "56e75a9037bdd419002c3ec8": "detention",
+    "56e75a9037bdd419002c3ec9": "It requires the pupil to remain in school at a given time in the school day",
+    "56e75a9037bdd419002c3eca": "quietly",
+    "56e75a9037bdd419002c3ecb": "lines or a punishment essay",
+    "56e75b8237bdd419002c3ed1": "assertive",
+    "56e75b8237bdd419002c3ed2": "immediate and fair punishment",
+    "56e75b8237bdd419002c3ed3": "firm, clear",
+    "56e75b8237bdd419002c3ed4": "inappropriate behavior",
+    "56e75b8237bdd419002c3ed5": "respect their students",
+    "56e75d5037bdd419002c3ef5": "teachers and parents",
+    "56e75d5037bdd419002c3ef6": "East Asia",
+    "56e75d5037bdd419002c3ef7": "weakness in school discipline",
+    "56e75d5037bdd419002c3ef8": "assertive and confrontational",
+    "56e75e4500c9c71400d7702c": "Japan",
+    "56e75e4500c9c71400d7702d": "Japan",
+    "56e75e4500c9c71400d7702e": "Japan",
+    "56e75e4500c9c71400d7702f": "Japan",
+    "56e75f5500c9c71400d7703a": "40 to 50 students",
+    "56e75f5500c9c71400d7703b": "what is being taught",
+    "56e75f5500c9c71400d7703c": "motivated students",
+    "56e75f5500c9c71400d7703d": "motivated students",
+    "56e75f5500c9c71400d7703e": "motivated students",
+    "56e7611500c9c71400d77054": "dictatorial",
+    "56e7611500c9c71400d77055": "governments and schools alike",
+    "56e7611500c9c71400d77056": "persuasion and negotiation",
+    "56e7611500c9c71400d77057": "easier and more efficient",
+    "56e7611500c9c71400d77058": "good, clear laws",
+    "56e761d037bdd419002c3f13": "positive learning experience",
+    "56e761d037bdd419002c3f14": "their passion",
+    "56e761d037bdd419002c3f15": "attempt to find new invigoration for the course materials on a daily basis",
+    "56e761d037bdd419002c3f17": "higher",
+    "56e762fe00c9c71400d77072": "nonverbal expressions of enthusiasm",
+    "56e762fe00c9c71400d77073": "read lecture material",
+    "56e762fe00c9c71400d77074": "nonverbal expressions of enthusiasm",
+    "56e762fe00c9c71400d77075": "experimental",
+    "56e762fe00c9c71400d77076": "higher",
+    "56e763e800c9c71400d77086": "self-determined",
+    "56e763e800c9c71400d77087": "enthusiasm",
+    "56e763e800c9c71400d77088": "emotional contagion",
+    "56e763e800c9c71400d77089": "Teacher enthusiasm",
+    "56e764e200c9c71400d7708e": "student-teacher relationships",
+    "56e764e200c9c71400d7708f": "beneficial",
+    "56e764e200c9c71400d77090": "the goals he receives from his superior",
+    "56e764e200c9c71400d77091": "aligning his personal goals with his academic goals",
+    "56e764e200c9c71400d77092": "student motivation and attitudes towards school",
+    "56e765ba00c9c71400d770a2": "friendly and supportive",
+    "56e765ba00c9c71400d770a4": "friendly and supportive",
+    "56e765ba00c9c71400d770a5": "interacting and working directly",
+    "56e765ba00c9c71400d770a6": "Effective",
+    "56e7667e37bdd419002c3f49": "enthusiasm about teaching, enthusiasm about the students, and enthusiasm about the subject matter",
+    "56e7667e37bdd419002c3f4a": "enthusiastic",
+    "56e7667e37bdd419002c3f4b": "the student",
+    "56e7667e37bdd419002c3f4c": "very influential",
+    "56e7667e37bdd419002c3f4d": "teaching",
+    "56e7673a37bdd419002c3f53": "sexual misconduct",
+    "56e7673a37bdd419002c3f54": "9.6%",
+    "56e7673a37bdd419002c3f55": "United States",
+    "56e7673a37bdd419002c3f56": "sometime during their educational career",
+    "56e7673a37bdd419002c3f57": "American Association of University Women",
+    "56e7683d00c9c71400d770ca": "England",
+    "56e7683d00c9c71400d770cb": "not necessarily a teacher",
+    "56e7683d00c9c71400d770cd": "2,869",
+    "56e7683d00c9c71400d770ce": "AAUW",
+    "56e768ce37bdd419002c3f67": "United States",
+    "56e768ce37bdd419002c3f69": "increased scrutiny on teacher misconduct",
+    "56e769dc00c9c71400d770e8": "Fears of being labelled a pedophile or hebephile",
+    "56e769dc00c9c71400d770e9": "Chris Keates",
+    "56e769dc00c9c71400d770ea": "child protection and parental rights groups",
+    "56e769dc00c9c71400d770eb": "a shortage",
+    "56e769dc00c9c71400d770ec": "sex offenders register",
+    "56e76abf37bdd419002c3f75": "occupational stress",
+    "56e76abf37bdd419002c3f76": "long hours",
+    "56e76abf37bdd419002c3f77": "occupational burnout",
+    "56e76abf37bdd419002c3f78": "Stress",
+    "56e76abf37bdd419002c3f79": "occupational stress",
+    "56e76b8337bdd419002c3f7f": "42%",
+    "56e76b8337bdd419002c3f80": "UK",
+    "56e76b8337bdd419002c3f81": "twice",
+    "56e76b8337bdd419002c3f82": "2012",
+    "56e76b8337bdd419002c3f83": "average workers",
+    "56e76c6a00c9c71400d7710e": "several",
+    "56e76c6a00c9c71400d7710f": "Organizational interventions",
+    "56e76c6a00c9c71400d77110": "Individual-level interventions",
+    "56e76c6a00c9c71400d77111": "occupational stress",
+    "56e76c6a00c9c71400d77112": "Organizational",
+    "56e76d6537bdd419002c3f93": "a university or college",
+    "56e76d6537bdd419002c3f94": "certification",
+    "56e76d6537bdd419002c3f95": "elementary school education certificate",
+    "56e76d6537bdd419002c3f96": "background check and psychiatric evaluation",
+    "56e76d6537bdd419002c3f97": "US",
+    "56e76de800c9c71400d77122": "individual states and territories",
+    "56e76de800c9c71400d77123": "three",
+    "56e76de800c9c71400d77124": "secondary schools/high schools",
+    "56e76de800c9c71400d77125": "universities and/or TAFE colleges",
+    "56e76de800c9c71400d77126": "three-tier model",
+    "56e76ea737bdd419002c3f9d": "post-secondary degree Bachelor's Degree",
+    "56e76ea737bdd419002c3f9e": "a second Bachelor's Degree such as a Bachelor of Education",
+    "56e76ea737bdd419002c3fa1": "private sector, businesses and sponsors",
+    "56e76f7000c9c71400d7712c": "civil servants",
+    "56e76f7000c9c71400d7712d": "Germany",
+    "56e76f7000c9c71400d7712e": "Grundschule",
+    "56e76f7000c9c71400d7712f": "civil servants' salary index scale",
+    "56e76f7000c9c71400d77130": "Hauptschule",
+    "56e7714a00c9c71400d77137": "Extra pay",
+    "56e7714a00c9c71400d77138": "\u20ac27,814 p.a.",
+    "56e7714a00c9c71400d77139": "\u20ac53,423",
+    "56e7714a00c9c71400d7713a": "over \u20ac90,000",
+    "56e7721500c9c71400d77140": "Teaching Council",
+    "56e7721500c9c71400d77141": "Section 30",
+    "56e7721500c9c71400d77142": "2001",
+    "56e7721500c9c71400d77143": "Oireachtas funds",
+    "56e772bf37bdd419002c3fbb": "2006",
+    "56e772bf37bdd419002c3fbc": "Existing staff",
+    "56e772bf37bdd419002c3fbe": "on a phased basis",
+    "56e772bf37bdd419002c3fbf": "those who refuse vetting",
+    "56e773fa00c9c71400d7714a": "\u00a341,004",
+    "56e773fa00c9c71400d7714b": "experience and extra responsibilities",
+    "56e773fa00c9c71400d7714c": "\u00a320,980",
+    "56e773fa00c9c71400d7714d": "bachelor's degree",
+    "56e773fa00c9c71400d7714e": "September 2007",
+    "56e7752337bdd419002c3fd5": "alternative licensing programs",
+    "56e7752337bdd419002c3fd6": "hard-to-fill",
+    "56e7752337bdd419002c3fd7": "vary by geographic area and subject taught",
+    "56e7752337bdd419002c3fd8": "Excellent job opportunities",
+    "56e7752337bdd419002c3fd9": "secondary school teachers",
+    "56e775ec00c9c71400d7715c": "General Teaching Council for Scotland",
+    "56e775ec00c9c71400d7715d": "Teaching",
+    "56e775ec00c9c71400d7715e": "seven",
+    "56e775ec00c9c71400d7715f": "Provisional Registration",
+    "56e775ec00c9c71400d77160": "a year",
+    "56e7770037bdd419002c3fdf": "April 2008",
+    "56e7770037bdd419002c3fe0": "\u00a320,427",
+    "56e7770037bdd419002c3fe1": "\u00a332,583",
+    "56e7770037bdd419002c3fe2": "complete the modules",
+    "56e7770037bdd419002c3fe3": "trade unions",
+    "56e777e500c9c71400d77176": "Wales",
+    "56e777e500c9c71400d77177": "Welsh",
+    "56e777e500c9c71400d77178": "until the age of 16",
+    "56e777e500c9c71400d77179": "22",
+    "56e777e500c9c71400d7717a": "all age groups",
+    "56e7788200c9c71400d77180": "trade unions",
+    "56e7788200c9c71400d77181": "falling",
+    "56e7788200c9c71400d77182": "between 2005 and 2010",
+    "56e7788200c9c71400d77183": "trade unions",
+    "56e7788200c9c71400d77184": "attacks on teachers in Welsh schools which reached an all-time high between 2005 and 2010.",
+    "56e7796637bdd419002c3ffd": "each state",
+    "56e7796637bdd419002c3ffe": "ten years",
+    "56e7796637bdd419002c3fff": "bachelor's degree",
+    "56e7796637bdd419002c4000": "Many charter schools",
+    "56e7796637bdd419002c4001": "No Child Left Behind",
+    "56e77a8700c9c71400d7718a": "relatively low salaries",
+    "56e77a8700c9c71400d7718b": "average teacher salaries",
+    "56e77a8700c9c71400d7718c": "state, relative cost of living, and grade taught",
+    "56e77a8700c9c71400d7718d": "elementary school teachers",
+    "56e77a8700c9c71400d7718e": "TeachersPayTeachers.com",
+    "56e77b8c00c9c71400d77194": "many",
+    "56e77b8c00c9c71400d77196": "Protestant",
+    "56e77b8c00c9c71400d77197": "not always a bishop",
+    "56e77b8c00c9c71400d77198": "(Roman) Catholic, (Eastern) Orthodox Catholic, and Protestant/Non-Denominational",
+    "56e77c6737bdd419002c401d": "LDS Church",
+    "56e77c6737bdd419002c401e": "a trusted friend",
+    "56e77c6737bdd419002c401f": "spiritual",
+    "56e77c6737bdd419002c4020": "the husband and father",
+    "56e77c6737bdd419002c4021": "father of the house",
+    "56e77cee00c9c71400d771a8": "guru",
+    "56e77cee00c9c71400d771a9": "high",
+    "56e77cee00c9c71400d771aa": "disciples",
+    "56e77cee00c9c71400d771ab": "West",
+    "56e77da237bdd419002c403b": "Lama",
+    "56e77da237bdd419002c403c": "be reborn",
+    "56e77da237bdd419002c403d": "Tulku",
+    "56e77da237bdd419002c403e": "many times",
+    "56e77da237bdd419002c403f": "phowa and siddhi",
+    "56e77e4a00c9c71400d771b0": "ulemas",
+    "56e77e4a00c9c71400d771b1": "teachers",
+    "56e77e4a00c9c71400d771b2": "Sufism",
+    "56e77e4a00c9c71400d771b3": "actions-oriented, e.g. the Five Pillars of Islam",
+    "56e77e4a00c9c71400d771b4": "Qutb",
+    "56f7c651aef2371900625bf5": "German",
+    "56f7c651aef2371900625bf6": "18 February 1546",
+    "56f7c651aef2371900625bf7": "Late Medieval Catholic Church",
+    "56f7c651aef2371900625bf8": "freedom from God's punishment for sin",
+    "56f7c651aef2371900625bf9": "excommunication",
+    "56f7c8aba6d7ea1400e1727a": "a free gift of God's grace",
+    "56f7c8aba6d7ea1400e1727b": "faith in Jesus Christ",
+    "56f7c8aba6d7ea1400e1727c": "the Pope",
+    "56f7c8aba6d7ea1400e1727d": "the Bible",
+    "56f7c8aba6d7ea1400e1727e": "a holy priesthood",
+    "56f7cb10a6d7ea1400e17284": "Bible",
+    "56f7cb10a6d7ea1400e17285": "a standard version",
+    "56f7cb10a6d7ea1400e17286": "Tyndale Bible",
+    "56f7cb10a6d7ea1400e17287": "singing in churches",
+    "56f7cb10a6d7ea1400e17288": "Protestant clergy to marry",
+    "56f7cdc2a6d7ea1400e1728e": "10 November 1483",
+    "56f7cdc2a6d7ea1400e1728f": "Eisleben, Saxony",
+    "56f7cdc2a6d7ea1400e17290": "Holy Roman Empire",
+    "56f7cdc2a6d7ea1400e17291": "Catholic",
+    "56f7cdc2a6d7ea1400e17292": "lawyer",
+    "56f7e9caaef2371900625c56": "University of Erfurt",
+    "56f7e9caaef2371900625c57": "a beerhouse and whorehouse",
+    "56f7e9caaef2371900625c58": "four",
+    "56f7e9caaef2371900625c59": "a day of rote learning and often wearying spiritual exercises.\"",
+    "56f7e9caaef2371900625c5a": "1505",
+    "56f7eba8a6d7ea1400e172cd": "law school",
+    "56f7eba8a6d7ea1400e172ce": "uncertainty",
+    "56f7eba8a6d7ea1400e172cf": "theology and philosophy",
+    "56f7eba8a6d7ea1400e172d0": "by experience",
+    "56f7eba8a6d7ea1400e172d1": "God",
+    "56f7eddca6d7ea1400e172d7": "death and divine judgment",
+    "56f7eddca6d7ea1400e172d8": "2 July 1505",
+    "56f7eddca6d7ea1400e172d9": "Erfurt",
+    "56f7eddca6d7ea1400e172da": "deaths of two friends",
+    "56f7eddca6d7ea1400e172db": "Luther's education",
+    "56f7ef96aef2371900625c74": "Augustinian order",
+    "56f7ef96aef2371900625c75": "deep spiritual despair",
+    "56f7ef96aef2371900625c76": "the jailer and hangman of my poor soul",
+    "56f7ef96aef2371900625c77": "Johann von Staupitz",
+    "56f7ef96aef2371900625c78": "a change of heart",
+    "56f7f15aa6d7ea1400e172eb": "1507",
+    "56f7f15aa6d7ea1400e172ec": "von Staupitz",
+    "56f7f15aa6d7ea1400e172ed": "1508",
+    "56f7f15aa6d7ea1400e172ee": "9 March 1508",
+    "56f7f15aa6d7ea1400e172ef": "theology",
+    "56f7f2e0aef2371900625cb0": "19 October 1512",
+    "56f7f2e0aef2371900625cb1": "21 October 1512",
+    "56f7f2e0aef2371900625cb2": "Doctor in Bible",
+    "56f7f2e0aef2371900625cb3": "University of Wittenberg",
+    "56f7f2e0aef2371900625cb4": "Doctor of Theology",
+    "56f7fde8a6d7ea1400e17367": "1516",
+    "56f7fde8a6d7ea1400e17368": "to raise money to rebuild St. Peter's Basilica in Rome",
+    "56f7fde8a6d7ea1400e17369": "Roman Catholic",
+    "56f7fde8a6d7ea1400e1736a": "charity and good works",
+    "56f7fde8a6d7ea1400e1736b": "justification rather depends only on such faith as is active in charity and good works",
+    "56f7ffadaef2371900625d5e": "31 October 1517",
+    "56f7ffadaef2371900625d5f": "Albert of Mainz",
+    "56f7ffadaef2371900625d60": "Ninety-Five Theses",
+    "56f7ffadaef2371900625d61": "Hans Hillerbrand",
+    "56f7ffadaef2371900625d62": "Thesis 86",
+    "56f80143aef2371900625d68": "Johann Tetzel",
+    "56f80143aef2371900625d69": "soon as the coin in the coffer rings",
+    "56f80143aef2371900625d6a": "Luther",
+    "56f80143aef2371900625d6b": "Johann Tetzel",
+    "56f802c6a6d7ea1400e17377": "God",
+    "56f802c6a6d7ea1400e17378": "salvation",
+    "56f802c6a6d7ea1400e17379": "all punishments",
+    "56f802c6a6d7ea1400e1737a": "indulgences",
+    "56f802c6a6d7ea1400e1737b": "Christ",
+    "56f8046faef2371900625d71": "Tetzel",
+    "56f8046faef2371900625d72": "a reflection of his capacity to exaggerate",
+    "56f8046faef2371900625d73": "indulgences for the dead",
+    "56f8046faef2371900625d74": "Catholic dogma of the time",
+    "56f80604a6d7ea1400e17387": "the posting on the door",
+    "56f80604a6d7ea1400e17388": "the posting on the door",
+    "56f80604a6d7ea1400e17389": "Philipp Melanchthon",
+    "56f80604a6d7ea1400e1738a": "Wittenberg",
+    "56f80604a6d7ea1400e1738b": "little foundation in truth",
+    "56f8074faef2371900625d79": "January 1518",
+    "56f8074faef2371900625d7a": "printing press",
+    "56f8074faef2371900625d7b": "friends",
+    "56f8074faef2371900625d7c": "two weeks",
+    "56f8074faef2371900625d7d": "two months",
+    "56f8094aa6d7ea1400e17391": "1519",
+    "56f8094aa6d7ea1400e17392": "Students",
+    "56f8094aa6d7ea1400e17393": "early",
+    "56f8094aa6d7ea1400e17394": "1520",
+    "56f8094aa6d7ea1400e17395": "On the Freedom of a Christian",
+    "56f80ad1a6d7ea1400e1739b": "lectured on the Psalms, the books of Hebrews, Romans, and Galatians",
+    "56f80ad1a6d7ea1400e1739c": "penance and righteousness",
+    "56f80ad1a6d7ea1400e1739d": "the church was corrupt",
+    "56f80ad1a6d7ea1400e1739e": "several of the central truths of Christianity",
+    "56f80ad1a6d7ea1400e1739f": "This one and firm rock, which we call the doctrine of justification",
+    "56f80ccfaef2371900625d83": "God",
+    "56f80ccfaef2371900625d84": "1525",
+    "56f80ccfaef2371900625d85": "a gift from God",
+    "56f80ccfaef2371900625d86": "Smalcald Articles",
+    "56f80ccfaef2371900625d87": "faith",
+    "56f80e1daef2371900625d8d": "\"Christ and His salvation\"",
+    "56f80e1daef2371900625d8e": "Christ and His salvation",
+    "56f80e1daef2371900625d8f": "the Reformation",
+    "56f80e1daef2371900625d90": "two",
+    "56f80fdfaef2371900625d95": "Archbishop Albrecht of Mainz and Magdeburg",
+    "56f80fdfaef2371900625d96": "Rome",
+    "56f80fdfaef2371900625d97": "a papal dispensation for his tenure of more than one bishopric",
+    "56f80fdfaef2371900625d98": "one half",
+    "56f80fdfaef2371900625d99": "December 1517",
+    "56f811bdaef2371900625d9f": "Pope Leo X",
+    "56f811bdaef2371900625da0": "a series of papal theologians and envoys",
+    "56f811bdaef2371900625da1": "October 1518",
+    "56f811bdaef2371900625da2": "historistical interpretation of Bible prophecy concluded that the papacy was the Antichrist",
+    "56f811bdaef2371900625da3": "theologians and envoys",
+    "56f81393aef2371900625da9": "January 1519",
+    "56f81393aef2371900625daa": "remain silent",
+    "56f81393aef2371900625dab": "Johann Eck",
+    "56f81393aef2371900625dac": "Matthew 16:18",
+    "56f81393aef2371900625dad": "Jan Hus",
+    "56f81537aef2371900625db3": "15 June 1520",
+    "56f81537aef2371900625db4": "recanted 41 sentences drawn from his writings",
+    "56f81537aef2371900625db5": "60 days",
+    "56f81537aef2371900625db6": "Karl von Miltitz",
+    "56f81537aef2371900625db7": "3 January 1521",
+    "56f8225ea6d7ea1400e173f1": "secular authorities",
+    "56f8225ea6d7ea1400e173f2": "18 April 1521",
+    "56f8225ea6d7ea1400e173f3": "the estates of the Holy Roman Empire",
+    "56f8225ea6d7ea1400e173f4": "Emperor Charles V",
+    "56f8225ea6d7ea1400e173f5": "Prince Frederick III",
+    "56f82454a6d7ea1400e173fb": "Johann Eck",
+    "56f82454a6d7ea1400e173fc": "Archbishop of Trier",
+    "56f82454a6d7ea1400e173fd": "whether he stood by their contents",
+    "56f82454a6d7ea1400e173fe": "the next day",
+    "56f82454a6d7ea1400e173ff": "whether he stood by their contents",
+    "56f82549a6d7ea1400e17415": "raised his arm",
+    "56f82549a6d7ea1400e17416": "a knight winning a bout.\"",
+    "56f82549a6d7ea1400e17417": "Michael Mullett",
+    "56f82549a6d7ea1400e17418": "world classic of epoch-making oratory",
+    "56f826a7a6d7ea1400e17427": "recant his writings",
+    "56f826a7a6d7ea1400e17428": "Luther",
+    "56f826a7a6d7ea1400e17429": "they were inserted before \"May God help me\" only in later versions of the speech and not recorded in witness accounts of the proceedings",
+    "56f826a7a6d7ea1400e1742a": "more dramatic",
+    "56f827caa6d7ea1400e17439": "private conferences were held to determine Luther's fate. The Emperor presented the final draft of the Edict of Worms",
+    "56f827caa6d7ea1400e1743a": "25 May 1521",
+    "56f827caa6d7ea1400e1743b": "The Emperor",
+    "56f827caa6d7ea1400e1743c": "literature",
+    "56f827caa6d7ea1400e1743d": "kill",
+    "56f82989aef2371900625e6b": "disappearance",
+    "56f82989aef2371900625e6c": "Wartburg Castle at Eisenach",
+    "56f82989aef2371900625e6d": "my Patmos",
+    "56f82989aef2371900625e6e": "New Testament",
+    "56f82989aef2371900625e6f": "a renewed attack",
+    "56f82b25a6d7ea1400e17457": "a sin",
+    "56f82b25a6d7ea1400e17458": "cannot be earned",
+    "56f82b25a6d7ea1400e17459": "1 August 1521",
+    "56f82b25a6d7ea1400e1745a": "trust in Christ",
+    "56f82b25a6d7ea1400e1745b": "justice resides",
+    "56f84485aef2371900625f71": "summer of 1521",
+    "56f84485aef2371900625f72": "idolatry the idea that the mass is a sacrifice, asserting instead that it is a gift",
+    "56f84485aef2371900625f73": "a gift",
+    "56f84485aef2371900625f74": "private confession and absolution",
+    "56f84485aef2371900625f75": "without sin",
+    "56f845dba6d7ea1400e17519": "prophetic faith",
+    "56f845dba6d7ea1400e1751a": "1521",
+    "56f845dba6d7ea1400e1751b": "Daniel 8:9\u201312, 23\u201325",
+    "56f845dba6d7ea1400e1751c": "the Little Horn",
+    "56f845dba6d7ea1400e1751d": "The antichrist of 2 Thessalonians 2",
+    "56f84760aef2371900625f81": "Gabriel Zwilling",
+    "56f84760aef2371900625f82": "June 1521",
+    "56f84760aef2371900625f83": "disturbances",
+    "56f84760aef2371900625f84": "Zwickau prophets",
+    "56f84760aef2371900625f85": "the town council",
+    "56f848e0a6d7ea1400e1752f": "6 March 1522",
+    "56f848e0a6d7ea1400e17530": "trust God's word",
+    "56f848e0a6d7ea1400e17531": "preached eight sermons",
+    "56f848e0a6d7ea1400e17532": "Invocavit Sermons",
+    "56f848e0a6d7ea1400e17533": "trust God's word",
+    "56f84a60aef2371900625f9d": "immediate",
+    "56f84a60aef2371900625f9e": "Jerome Schurf",
+    "56f84a60aef2371900625f9f": "sixth",
+    "56f84a60aef2371900625fa0": "misguided people into the way of the truth",
+    "56f84a60aef2371900625fa1": "misguided",
+    "56f84b68aef2371900625fa7": "public order",
+    "56f84b68aef2371900625fa8": "conservative force",
+    "56f84b68aef2371900625fa9": "Zwickau prophets",
+    "56f84b68aef2371900625faa": "social unrest and violence",
+    "56f84b68aef2371900625fab": "reformers",
+    "56f84d33aef2371900625fb1": "Zwickau prophet",
+    "56f84d33aef2371900625fb2": "German Peasants' War",
+    "56f84d33aef2371900625fb3": "1524\u201325",
+    "56f84d33aef2371900625fb4": "support an attack on the upper classes in general",
+    "56f84d33aef2371900625fb5": "upper classes",
+    "56f84e63aef2371900625fc1": "temporal authorities",
+    "56f84e63aef2371900625fc2": "Thuringia",
+    "56f84e63aef2371900625fc3": "mad dogs",
+    "56f84e63aef2371900625fc4": "condemned the violence as the devil's work",
+    "56f84e63aef2371900625fc5": "nobles",
+    "56f8507fa6d7ea1400e1754b": "three",
+    "56f8507fa6d7ea1400e1754c": "ignoring Christ's counsel to \"Render unto Caesar the things that are Caesar's\"",
+    "56f8507fa6d7ea1400e1754d": "God",
+    "56f8507fa6d7ea1400e1754e": "Divine Right of Kings",
+    "56f8507fa6d7ea1400e1754f": "death in body and soul",
+    "56f851b1a6d7ea1400e1755d": "laid down their weapons",
+    "56f851b1a6d7ea1400e1755e": "Swabian League",
+    "56f851b1a6d7ea1400e1755f": "15 May 1525",
+    "56f851b1a6d7ea1400e17560": "Battle of Frankenhausen on 15 May 1525, followed by M\u00fcntzer's execution",
+    "56f851b1a6d7ea1400e17561": "secular powers",
+    "56f852fba6d7ea1400e1756d": "Katharina von Bora",
+    "56f852fba6d7ea1400e1756e": "herring barrels",
+    "56f852fba6d7ea1400e1756f": "41",
+    "56f852fba6d7ea1400e17570": "41",
+    "56f852fba6d7ea1400e17571": "April 1523",
+    "56f8541da6d7ea1400e17577": "13 June 1525",
+    "56f8541da6d7ea1400e17578": "evening",
+    "56f8541da6d7ea1400e17579": "wedding banquet",
+    "56f8541da6d7ea1400e1757a": "27 June",
+    "56f8541da6d7ea1400e1757b": "Bugenhagen",
+    "56f855caaef2371900625ff3": "clerical marriage",
+    "56f855caaef2371900625ff4": "clerical",
+    "56f855caaef2371900625ff5": "Biblical grounds",
+    "56f855caaef2371900625ff6": "vows of celibacy",
+    "56f855caaef2371900625ff7": "reckless",
+    "56f8575aaef2371900625ffd": "1525\u201332",
+    "56f8575aaef2371900625ffe": "a former monastery",
+    "56f8575aaef2371900625fff": "six",
+    "56f8575aaef2371900626000": "the riches of Croesus",
+    "56f8575aaef2371900626001": "farming the land",
+    "56f8599aa6d7ea1400e175af": "choosing their own ministers",
+    "56f8599aa6d7ea1400e175b0": "a supervisory church body, laid down a new form of worship service",
+    "56f8599aa6d7ea1400e175b1": "a supervisory church body",
+    "56f8599aa6d7ea1400e175b2": "two catechisms",
+    "56f8599aa6d7ea1400e175b3": "a theology of the cross",
+    "56f85bb8aef237190062600f": "extreme change",
+    "56f85bb8aef2371900626010": "Electorate of Saxony",
+    "56f85bb8aef2371900626011": "adviser",
+    "56f85bb8aef2371900626012": "John the Steadfast",
+    "56f85bb8aef2371900626013": "questionable",
+    "56f85cf0aef2371900626019": "early 1526",
+    "56f85cf0aef237190062601a": "Latin Mass",
+    "56f85cf0aef237190062601b": "simple people",
+    "56f85cf0aef237190062601c": "everything that smacks of sacrifice\"",
+    "56f85cf0aef237190062601d": "freedom",
+    "56f85e71a6d7ea1400e175c3": "1527",
+    "56f85e71a6d7ea1400e175c4": "their visitation of the Electorate of Saxony",
+    "56f85e71a6d7ea1400e175c5": "Christian education",
+    "56f85e71a6d7ea1400e175c6": "Christian doctrine",
+    "56f85e71a6d7ea1400e175c7": "teaching",
+    "56f86680a6d7ea1400e175cd": "catechism",
+    "56f86680a6d7ea1400e175ce": "1529",
+    "56f86680a6d7ea1400e175cf": "congregations",
+    "56f86680a6d7ea1400e175d0": "the people themselves",
+    "56f86680a6d7ea1400e175d1": "questions and answers",
+    "56f867e3a6d7ea1400e175d7": "The catechism",
+    "56f867e3a6d7ea1400e175d8": "The catechism",
+    "56f867e3a6d7ea1400e175d9": "the Catechism",
+    "56f867e3a6d7ea1400e175da": "The Small Catechism",
+    "56f867e3a6d7ea1400e175db": "Bible",
+    "56f86966aef2371900626053": "Small Catechism",
+    "56f86966aef2371900626054": "Larger Catechism",
+    "56f86966aef2371900626055": "Apostles' Creed in simpler, more personal, Trinitarian language",
+    "56f86966aef2371900626056": "as persons to be known",
+    "56f86966aef2371900626057": "the Father",
+    "56f86b44a6d7ea1400e175f5": "1522",
+    "56f86b44a6d7ea1400e175f6": "1534",
+    "56f86b44a6d7ea1400e175f7": "translation",
+    "56f86b44a6d7ea1400e175f8": "alone",
+    "56f86b44a6d7ea1400e175f9": "Faith",
+    "56f86d30a6d7ea1400e17605": "Saxon chancellery",
+    "56f86d30a6d7ea1400e17606": "northern and southern",
+    "56f86d30a6d7ea1400e17607": "northern and southern Germans. He intended his vigorous, direct language to make the Bible accessible to everyday Germans",
+    "56f86d30a6d7ea1400e17608": "we are removing impediments and difficulties so that other people may read it without hindrance",
+    "56f86d30a6d7ea1400e17609": "impediments and difficulties",
+    "56f86e91aef2371900626067": "German-language publications",
+    "56f86e91aef2371900626068": "Bible translation",
+    "56f86e91aef2371900626069": "the evolution of the German language and literature",
+    "56f86e91aef237190062606a": "Lucas Cranach",
+    "56f86e91aef237190062606b": "William Tyndale",
+    "56f87000aef2371900626071": "hymn-writer",
+    "56f87000aef2371900626072": "high art and folk music",
+    "56f87000aef2371900626073": "German hymns",
+    "56f87000aef2371900626074": "a lute",
+    "56f87000aef2371900626075": "waldzither",
+    "56f8720eaef237190062608f": "the unfolding Reformation",
+    "56f8720eaef2371900626090": "for Lutheran views",
+    "56f8720eaef2371900626091": "\"Ein neues Lied wir heben an\"",
+    "56f8720eaef2371900626092": "John C. Messenger",
+    "56f8720eaef2371900626093": "\"Ein neues Lied wir heben an\"",
+    "56f87392aef2371900626099": "1524",
+    "56f87392aef237190062609a": "the Apostles' Creed in the Small Catechism",
+    "56f87392aef237190062609b": "the Small Catechism",
+    "56f87392aef237190062609c": "German creedal hymn",
+    "56f87392aef237190062609d": "difficulty of its tune",
+    "56f87531a6d7ea1400e1766f": "1538",
+    "56f87531a6d7ea1400e17670": "one stanza for each of the seven prayer petitions, plus opening and closing stanzas",
+    "56f87531a6d7ea1400e17671": "specific catechism questions",
+    "56f87531a6d7ea1400e17672": "multiple revisions",
+    "56f87531a6d7ea1400e17673": "opening and closing stanzas",
+    "56f87760aef23719006260cc": "1523",
+    "56f87760aef23719006260cd": "Psalm 130",
+    "56f87760aef23719006260ce": "write psalm-hymns for use in German worship",
+    "56f87760aef23719006260cf": "Achtliederbuch",
+    "56f87760aef23719006260d0": "essential Reformation doctrine",
+    "56f879bdaef23719006260de": "Hauptlied",
+    "56f879bdaef23719006260df": "Veni redemptor gentium",
+    "56f879bdaef23719006260e0": "Now come, Savior of the gentiles), based on Veni redemptor gentium, became the main hymn",
+    "56f879bdaef23719006260e1": "two",
+    "56f879bdaef23719006260e2": "German Te Deum",
+    "56f87e95aef237190062610a": "baptism",
+    "56f87e95aef237190062610b": "Johann Walter",
+    "56f87e95aef237190062610c": "prayer for grace",
+    "56f87e95aef237190062610d": "J. S. Bach",
+    "56f87e95aef237190062610e": "Halle",
+    "56f88025aef237190062611e": "early Lutheran hymnals",
+    "56f88025aef237190062611f": "four",
+    "56f88025aef2371900626120": "18",
+    "56f88025aef2371900626121": "24",
+    "56f88025aef2371900626122": "Achtliederbuch",
+    "56f881e3a6d7ea1400e176f3": "Johann Sebastian Bach",
+    "56f881e3a6d7ea1400e176f4": "several verses",
+    "56f881e3a6d7ea1400e176f5": "1707",
+    "56f881e3a6d7ea1400e176f6": "1724 to 1725",
+    "56f881e3a6d7ea1400e176f7": "1707, in his second annual cycle (1724 to 1725",
+    "56f8837aa6d7ea1400e176fd": "sleeps",
+    "56f8837aa6d7ea1400e176fe": "torments",
+    "56f8837aa6d7ea1400e176ff": "penitential suffering",
+    "56f8837aa6d7ea1400e17700": "rejected the existence of Purgatory, which involved Christian souls undergoing penitential suffering after death",
+    "56f8837aa6d7ea1400e17701": "in their graves and in heaven",
+    "56f884cba6d7ea1400e17707": "Franz Pieper",
+    "56f884cba6d7ea1400e17708": "Johann Gerhard",
+    "56f884cba6d7ea1400e17709": "Johann Gerhard",
+    "56f884cba6d7ea1400e1770a": "1755",
+    "56f88690a6d7ea1400e17723": "Genesis",
+    "56f88690a6d7ea1400e17724": "Francis Blackburne",
+    "56f88690a6d7ea1400e17725": "1765",
+    "56f88690a6d7ea1400e17726": "Gottfried Fritschel",
+    "56f88690a6d7ea1400e17727": "dreams",
+    "56f88c37aef2371900626176": "October 1529",
+    "56f88c37aef2371900626177": "Landgrave of Hesse",
+    "56f88c37aef2371900626178": "doctrinal unity in the emerging Protestant states",
+    "56f88c37aef2371900626179": "fourteen",
+    "56f88c37aef237190062617a": "the nature of the Eucharist \u2013 the sacrament of the Lord's Supper",
+    "56f88eafaef2371900626194": "the significance of the words spoken by Jesus",
+    "56f88eafaef2371900626195": "the Real Presence of the body and blood of Christ",
+    "56f88eafaef2371900626196": "sacramental union",
+    "56f88eafaef2371900626197": "only spiritually or symbolically present",
+    "56f88eafaef2371900626198": "confrontational",
+    "56f8907faef23719006261b2": "1530",
+    "56f8907faef23719006261b3": "the Marburg Colloquy",
+    "56f8907faef23719006261b4": "Schmalkaldic League",
+    "56f8907faef23719006261b5": "The Swiss cities",
+    "56f8907faef23719006261b6": "George, Margrave of Brandenburg-Ansbach",
+    "56f895339e9bad19000a0177": "antithetical",
+    "56f895339e9bad19000a0178": "reason",
+    "56f895339e9bad19000a0179": "Reason in no way",
+    "56f895339e9bad19000a017a": "reason",
+    "56f895339e9bad19000a017b": "their different epistemological spheres",
+    "56f897059b226e1400dd0c5d": "conversion by proving that the Old Testament could be shown to speak of Jesus Christ",
+    "56f897059b226e1400dd0c5e": "large-scale Jewish conversion to Christianity",
+    "56f897059b226e1400dd0c5f": "the Jews",
+    "56f897059b226e1400dd0c60": "Anabaptists, Zwinglianism, and the papacy",
+    "56f897059b226e1400dd0c61": "1543",
+    "56f8989f9e9bad19000a01a5": "a scourge sent to punish Christians by God",
+    "56f8989f9e9bad19000a01a6": "to punish Christians by God",
+    "56f8989f9e9bad19000a01a7": "to punish Christians by God",
+    "56f8989f9e9bad19000a01a8": "the papacy",
+    "56f8989f9e9bad19000a01a9": "non-religious war",
+    "56f89a959b226e1400dd0c9d": "Qur'an",
+    "56f89a959b226e1400dd0c9e": "critical pamphlets",
+    "56f89a959b226e1400dd0c9f": "critical pamphlets on Islam",
+    "56f89a959b226e1400dd0ca0": "a tool of the devil",
+    "56f89a959b226e1400dd0ca1": "exposed to scrutiny",
+    "56f8a2969e9bad19000a022b": "God's wrath to Christians",
+    "56f8a2969e9bad19000a022c": "Agricola",
+    "56f8a2969e9bad19000a022d": "city hall",
+    "56f8a2969e9bad19000a022e": "with six series of theses",
+    "56f8a2969e9bad19000a022f": "open letter to C. G\u00fcttel Against the Antinomians, and his book On the Councils and the Church",
+    "56f8a4e99e9bad19000a0251": "second use of the law",
+    "56f8a4e99e9bad19000a0252": "second use of the law,\" that is, the law as the Holy Spirit's tool to work sorrow over sin in man's heart",
+    "56f8a4e99e9bad19000a0253": "everything that is used to work sorrow over sin",
+    "56f8a4e99e9bad19000a0254": "removing the three letters l-a-w from the church \u2013 does not eliminate the accusing law",
+    "56f8a4e99e9bad19000a0255": "essentially holy people",
+    "56f8a6969e9bad19000a025b": "live",
+    "56f8a6969e9bad19000a025c": "the Ten Commandments",
+    "56f8a6969e9bad19000a025d": "third",
+    "56f8a6969e9bad19000a025e": "an illustration of the Ten Commandments",
+    "56f8a6969e9bad19000a025f": "the Ten Commandments",
+    "56f8aa749b226e1400dd0da7": "baptism",
+    "56f8aa749b226e1400dd0da8": "The Ten Commandments",
+    "56f8aa749b226e1400dd0da9": "service to the neighbor",
+    "56f8ac579e9bad19000a02b9": "marry one of his wife's ladies-in-waiting",
+    "56f8ac579e9bad19000a02ba": "bigamy",
+    "56f8ac579e9bad19000a02bb": "one of his wife's ladies-in-waiting",
+    "56f8ac579e9bad19000a02bc": "landgrave himself",
+    "56f8ac579e9bad19000a02bd": "lasting damage",
+    "56f8ae099e9bad19000a02eb": "expelled",
+    "56f8ae099e9bad19000a02ec": "Jews",
+    "56f8ae099e9bad19000a02ed": "murder of Christ",
+    "56f8ae099e9bad19000a02ee": "the divinity of Jesus",
+    "56f8ae099e9bad19000a02ef": "convert them to Christianity",
+    "56f8afbc9b226e1400dd0e05": "Von den Juden und Ihren L\u00fcgen",
+    "56f8afbc9b226e1400dd0e06": "1543",
+    "56f8afbc9b226e1400dd0e07": "three years",
+    "56f8afbc9b226e1400dd0e08": "the devil's people",
+    "56f8afbc9b226e1400dd0e09": "a sanction for murder",
+    "56f8b2499b226e1400dd0e3b": "Jews",
+    "56f8b2499b226e1400dd0e3c": "Martin Luther",
+    "56f8b2499b226e1400dd0e3d": "doomed to perdition",
+    "56f8b2499b226e1400dd0e3e": "Luther's anti-Jewish works",
+    "56f8b2499b226e1400dd0e3f": "1580s",
+    "56f8b4d79b226e1400dd0e75": "Luther",
+    "56f8b4d79b226e1400dd0e76": "anti-Jewish rhetoric",
+    "56f8b4d79b226e1400dd0e77": "attacks on Jews",
+    "56f8b4d79b226e1400dd0e78": "Luther",
+    "56f8b4d79b226e1400dd0e79": "the most radically anti-Semitic tract ever published",
+    "56f8b7189e9bad19000a0389": "17 December 1941",
+    "56f8b7189e9bad19000a038a": "Luther",
+    "56f8b7189e9bad19000a038b": "Diarmaid MacCulloch",
+    "56f8b7189e9bad19000a038c": "seven Protestant regional church confederations",
+    "56f8b7189e9bad19000a038d": "the warner of his people against the Jews",
+    "56f8b9839e9bad19000a03bf": "opportunistic",
+    "56f8b9839e9bad19000a03c0": "church fathers' of anti-Semitism",
+    "56f8b9839e9bad19000a03c1": "modern hatred of the Jews",
+    "56f8b9839e9bad19000a03c2": "18th and 19th centuries",
+    "56f8b9839e9bad19000a03c3": "His position was entirely religious and in no respect racial",
+    "56f8bbb09e9bad19000a03db": "a new element",
+    "56f8bbb09e9bad19000a03dc": "Ronald Berger",
+    "56f8bbb09e9bad19000a03dd": "a \"hysterical and demonizing mentality\"",
+    "56f8bbb09e9bad19000a03de": "German Lutheran clergy and theologians",
+    "56f8bbb09e9bad19000a03df": "hostile publications",
+    "56f8bd509b226e1400dd0eed": "a declining state of mind",
+    "56f8bd509b226e1400dd0eee": "state of mind",
+    "56f8bd509b226e1400dd0eef": "vulgarity and violence",
+    "56f8bd509b226e1400dd0ef0": "the Jews and in diatribes against \"Turks\" (Muslims) and Catholics",
+    "56f8bd509b226e1400dd0ef1": "Luther's Last Battles: Politics and Polemics 1531\u201346",
+    "56f8c0cd9b226e1400dd0f35": "Since the 1980s",
+    "56f8c0cd9b226e1400dd0f36": "least prejudiced",
+    "56f8c0cd9b226e1400dd0f37": "Professor Richard (Dick) Geary",
+    "56f8c0cd9b226e1400dd0f38": "1928-1933",
+    "56f8c2ee9b226e1400dd0f45": "deteriorated further",
+    "56f8c2ee9b226e1400dd0f46": "Philip of Hesse incident",
+    "56f8c2ee9b226e1400dd0f47": "kidney and bladder stones, and arthritis",
+    "56f8c2ee9b226e1400dd0f48": "M\u00e9ni\u00e8re's disease, vertigo, fainting, tinnitus, and a cataract in one eye",
+    "56f8c2ee9b226e1400dd0f49": "angina",
+    "56f8c43d9b226e1400dd0f61": "poor physical health",
+    "56f8c43d9b226e1400dd0f62": "physical health made him short-tempered and even harsher in his writings and comments",
+    "56f8c43d9b226e1400dd0f63": "short-tempered and even harsher",
+    "56f8c43d9b226e1400dd0f64": "Katharina",
+    "56f8c43d9b226e1400dd0f65": "three",
+    "56f8c5909b226e1400dd0f7d": "Eisleben",
+    "56f8c5909b226e1400dd0f7e": "15 February 1546",
+    "56f8c5909b226e1400dd0f7f": "the obdurate Jews",
+    "56f8c5909b226e1400dd0f80": "all German territory",
+    "56f8c5909b226e1400dd0f81": "\"we want to practice Christian love toward them and pray that they convert,\"",
+    "56f8c7029e9bad19000a04a0": "Mansfeld",
+    "56f8c7029e9bad19000a04a1": "negotiations for a settlement",
+    "56f8c7029e9bad19000a04a2": "late 1545",
+    "56f8c7029e9bad19000a04a3": "early 1546",
+    "56f8c7029e9bad19000a04a4": "his siblings' families",
+    "56f8c8469e9bad19000a04c6": "17 February 1546",
+    "56f8c8469e9bad19000a04c7": "chest pains",
+    "56f8c8469e9bad19000a04c8": "Ps. 31:5",
+    "56f8c8469e9bad19000a04c9": "the common prayer of the dying",
+    "56f8c8469e9bad19000a04ca": "1 a.m",
+    "56f8c9719e9bad19000a04e0": "An apoplectic stroke",
+    "56f8c9719e9bad19000a04e1": "2:45 a.m",
+    "56f8c9719e9bad19000a04e2": "18 February 1546",
+    "56f8c9719e9bad19000a04e3": "the Castle Church in Wittenberg",
+    "56f8c9719e9bad19000a04e4": "Johannes Bugenhagen and Philipp Melanchthon",
+    "56f8ca289b226e1400dd1007": "A piece of paper",
+    "56f8ca289b226e1400dd1008": "Latin",
+    "56f8ca289b226e1400dd1009": "\"We are beggars,\"",
+    "56f8cc399e9bad19000a0514": "printed",
+    "56f8cc399e9bad19000a0515": "frail",
+    "56f8cc399e9bad19000a0516": "physically imposing",
+    "56f8cc399e9bad19000a0517": "frail Catholic saints",
+    "56f8cc399e9bad19000a0518": "1530s and 1540s",
+    "56f8ce779b226e1400dd103d": "18 February",
+    "56f8ce779b226e1400dd103e": "Church of England's Calendar of Saints",
+    "56f8ce779b226e1400dd103f": "31 October",
+    "56f8ce779b226e1400dd1040": "the Church of England's Calendar of Saints",
+    "56f8ce779b226e1400dd1041": "In the Church of England's Calendar of Saints he is commemorated on 31 October.",
+    "5705e26d75f01819005e76d4": "SoCal",
+    "5705e26d75f01819005e76d5": "10",
+    "5705e26d75f01819005e76d6": "economic center",
+    "5705e26d75f01819005e76d7": "demographics and economic",
+    "5705e26d75f01819005e76d8": "demographics and economic ties",
+    "5705e33f52bb89140068964c": "greater Southern California Megaregion",
+    "5705e33f52bb89140068964d": "11",
+    "5705e33f52bb89140068964e": "Las Vegas, Nevada",
+    "5705e33f52bb89140068964f": "Mexican border",
+    "5705e33f52bb891400689650": "Las Vegas, Nevada",
+    "5705e3f252bb89140068966a": "Pacific coast",
+    "5705e3f252bb89140068966b": "seven",
+    "5705e3f252bb89140068966c": "over 12 million",
+    "5705e3f252bb89140068966d": "El Centro area",
+    "5705e3f252bb89140068966e": "17.5 million",
+    "5705e4fe75f01819005e7704": "Colorado River",
+    "5705e4fe75f01819005e7705": "Colorado Desert",
+    "5705e4fe75f01819005e7706": "Mojave Desert",
+    "5705e4fe75f01819005e7707": "Mexico\u2013United States border",
+    "5705e63175f01819005e7720": "southern California",
+    "5705e63175f01819005e7721": "3,792,621",
+    "5705e63175f01819005e7722": "Los Angeles and San Diego",
+    "5705e63175f01819005e7723": "San Diego",
+    "5705e63175f01819005e7724": "south",
+    "5705e99452bb891400689688": "Los Angeles",
+    "5705e99452bb891400689689": "United States",
+    "5705e99452bb89140068968a": "counties of Los Angeles, Orange, San Diego, San Bernardino, and Riverside are the five most populous",
+    "5705e99452bb89140068968b": "15",
+    "5705e99452bb89140068968c": "Its counties of Los Angeles, Orange, San Diego, San Bernardino, and Riverside are the five most populous in the state",
+    "5705eb3375f01819005e7764": "Hollywood",
+    "5705eb3375f01819005e7765": "Los Angeles",
+    "5705eb3375f01819005e7766": "Walt Disney Company",
+    "5705eb3375f01819005e7767": "music",
+    "5705eb3375f01819005e7768": "Sony",
+    "5705ec1675f01819005e776e": "skateboard",
+    "5705ec1675f01819005e776f": "Tony Hawk",
+    "5705ec1675f01819005e7770": "Shaun White",
+    "5705ec1675f01819005e7771": "Oahu",
+    "5705ec1675f01819005e7772": "Transpacific Yacht Race",
+    "5705eccb52bb8914006896b8": "Palm Springs",
+    "5705eccb52bb8914006896b9": "popular beaches",
+    "5705eccb52bb8914006896ba": "southern California coast for its popular beaches, and the desert",
+    "5705eccb52bb8914006896bb": "open spaces",
+    "5705edcd52bb8914006896ca": "37\u00b0 9' 58.23\"",
+    "5705edcd52bb8914006896cb": "11",
+    "5705edcd52bb8914006896cc": "ten",
+    "5705edcd52bb8914006896cd": "Tehachapi Mountains",
+    "5705edcd52bb8914006896ce": "northern",
+    "5705eee952bb8914006896de": "Mexico",
+    "5705eee952bb8914006896df": "Alta California",
+    "5705eee952bb8914006896e0": "Monterey",
+    "5705eee952bb8914006896e1": "the Compromise of 1850",
+    "5705eee952bb8914006896e2": "free state",
+    "5705f09e75f01819005e77a4": "inequitable taxes",
+    "5705f09e75f01819005e77a5": "Cow Counties",
+    "5705f09e75f01819005e77a6": "three",
+    "5705f09e75f01819005e77a7": "75%",
+    "5705f09e75f01819005e77a8": "Milton Latham",
+    "5705f13d52bb8914006896f0": "Los Angeles Times",
+    "5705f13d52bb8914006896f1": "1900",
+    "5705f13d52bb8914006896f2": "1999",
+    "5705f13d52bb8914006896f3": "Imperial",
+    "5705f13d52bb8914006896f4": "seven",
+    "5705f36452bb891400689718": "regional tourism groups",
+    "5705f36452bb891400689719": "California State Automobile Association",
+    "5705f36452bb89140068971a": "three-region",
+    "5705f36452bb89140068971b": "transverse range",
+    "5705f36452bb89140068971c": "southern California region",
+    "5705f7c875f01819005e77dc": "third",
+    "5705f7c875f01819005e77dd": "vast areas",
+    "5705f7c875f01819005e77de": "suburban communities and use of automobiles and highways",
+    "5705f7c875f01819005e77df": "highways",
+    "5705f7c875f01819005e77e0": "megalopolis",
+    "5705fb7f52bb891400689750": "Camp Pendleton",
+    "5705fb7f52bb891400689751": "Inland Empire",
+    "5705fb7f52bb891400689752": "United States Census Bureau",
+    "5705fb7f52bb891400689753": "Orange",
+    "5705fb7f52bb891400689754": "1990s",
+    "5705fc3a52bb89140068976a": "Mediterranean",
+    "5705fc3a52bb89140068976b": "rain",
+    "5705fc3a52bb89140068976c": "90-60's",
+    "5705fc3a52bb89140068976d": "very rare",
+    "5705fc3a52bb89140068976e": "70-50's",
+    "5705fd8475f01819005e7841": "Pacific Ocean",
+    "5705fd8475f01819005e7840": "geologic, topographic, and natural ecosystem",
+    "5705fd8475f01819005e7842": "varied",
+    "5705fd8475f01819005e7843": "Peninsular Ranges",
+    "5705fd8475f01819005e7844": "interior valleys",
+    "5705fec152bb89140068977a": "10,000",
+    "5705fec152bb89140068977b": "10,000",
+    "5705fec152bb89140068977c": "6.7",
+    "5705fec152bb89140068977d": "property",
+    "5705fec152bb89140068977e": "over $20 billion",
+    "5705ffde52bb891400689784": "San Andreas Fault",
+    "5705ffde52bb891400689785": "6.7+",
+    "5705ffde52bb891400689786": "Puente Hills Fault",
+    "5705ffde52bb891400689787": "USGS",
+    "5705ffde52bb891400689788": "Earthquake occurrence",
+    "570602fa52bb89140068979e": "culture and atmosphere",
+    "570602fa52bb89140068979f": "global",
+    "570602fa52bb8914006897a0": "economic",
+    "570603c475f01819005e7882": "2010",
+    "570603c475f01819005e7883": "high growth rates",
+    "570603c475f01819005e7884": "10.0%",
+    "570603c475f01819005e7885": "tech-oriented",
+    "570603c475f01819005e7886": "southern California",
+    "5706074552bb8914006897d4": "Metropolitan Statistical Areas",
+    "5706074552bb8914006897d5": "two",
+    "5706074552bb8914006897d6": "five million",
+    "5706074552bb8914006897d7": "Southern Border Region",
+    "5706074552bb8914006897d8": "17,786,419",
+    "570607f575f01819005e78b4": "Los Angeles",
+    "570607f575f01819005e78b5": "100,000",
+    "570607f575f01819005e78b6": "34",
+    "570607f575f01819005e78b7": "100,000",
+    "570607f575f01819005e78b8": "Riverside",
+    "5706094b52bb8914006897de": "petroleum",
+    "5706094b52bb8914006897df": "Hollywood",
+    "5706094b52bb8914006897e0": "housing bubble",
+    "5706094b52bb8914006897e1": "diverse",
+    "5706094b52bb8914006897e2": "heavily impacted",
+    "57060a1175f01819005e78d2": "the 1920s",
+    "57060a1175f01819005e78d3": "richest",
+    "57060a1175f01819005e78d5": "citrus",
+    "57060a1175f01819005e78d4": "citrus",
+    "57060a1175f01819005e78d6": "aerospace",
+    "57060a6e52bb8914006897f8": "major business districts",
+    "57060a6e52bb8914006897f9": "Central business districts",
+    "57060a6e52bb8914006897fa": "Downtown Riverside",
+    "57060cc352bb89140068980e": "business districts",
+    "57060cc352bb89140068980f": "Los Angeles",
+    "57060cc352bb891400689810": "San Fernando Valley",
+    "57060cc352bb891400689811": "Los Angeles",
+    "57060df252bb891400689820": "business",
+    "57060df252bb891400689821": "Downtown Riverside",
+    "57060df252bb891400689822": "Hospitality Business/Financial Centre",
+    "57060eaf75f01819005e7910": "Orange County",
+    "57060eaf75f01819005e7911": "University of California, Irvine",
+    "57060eaf75f01819005e7912": "Irvine Tech Center",
+    "57060eaf75f01819005e7913": "South Coast Metro",
+    "57060eaf75f01819005e7914": "rapidly",
+    "57060f3e75f01819005e7922": "Downtown San Diego",
+    "57060f3e75f01819005e7923": "Northern San Diego",
+    "57060f3e75f01819005e7924": "North County",
+    "57060f3e75f01819005e7925": "Downtown San Diego",
+    "570610b275f01819005e792a": "Los Angeles International Airport",
+    "570610b275f01819005e792b": "international passenger volume",
+    "570610b275f01819005e792c": "third",
+    "570610b275f01819005e792d": "San Diego International Airport",
+    "570610b275f01819005e792e": "Van Nuys Airport",
+    "5706111a52bb89140068984c": "Metrolink",
+    "5706111a52bb89140068984d": "seven",
+    "5706111a52bb89140068984e": "Six",
+    "5706111a52bb89140068984f": "Orange",
+    "570611c475f01819005e793c": "Port of Los Angeles",
+    "570611c475f01819005e793d": "Port of Long Beach",
+    "570611c475f01819005e793e": "Southern California",
+    "5706139252bb891400689864": "The Tech Coast",
+    "5706139252bb891400689865": "research universities",
+    "5706139252bb891400689866": "private",
+    "5706139252bb891400689867": "5",
+    "5706139252bb891400689868": "12",
+    "5706143575f01819005e7950": "NFL",
+    "5706143575f01819005e7951": "NBA",
+    "5706143575f01819005e7952": "MLB",
+    "5706143575f01819005e7953": "Los Angeles Kings",
+    "5706143575f01819005e7954": "LA Galaxy",
+    "5706149552bb891400689880": "LA Galaxy",
+    "5706149552bb891400689881": "two",
+    "5706149552bb891400689882": "2014",
+    "5706149552bb891400689883": "StubHub Center",
+    "5706149552bb891400689884": "2018",
+    "570614ff52bb89140068988a": "College sports",
+    "570614ff52bb89140068988b": "USC Trojans",
+    "570614ff52bb89140068988c": "UCLA Bruins and the USC Trojans",
+    "570614ff52bb89140068988d": "Pac-12",
+    "570614ff52bb89140068988e": "Pac-12 Conference",
+    "5706155352bb891400689894": "Rugby",
+    "5706155352bb891400689895": "high school",
+    "5706155352bb891400689896": "official school sport",
+    "57092322efce8f15003a7db0": "BSkyB",
+    "57092322efce8f15003a7db1": "BSkyB",
+    "57092322efce8f15003a7db2": "2014",
+    "57092322efce8f15003a7db3": "British Sky Broadcasting Group plc",
+    "57092322efce8f15003a7db4": "British Sky Broadcasting Limited to Sky UK Limited",
+    "57094a79efce8f15003a7dc4": "2006",
+    "57094a79efce8f15003a7dc5": "two",
+    "57094a79efce8f15003a7dc6": "Setanta Sports",
+    "57094a79efce8f15003a7dc7": "\u00a31.3bn",
+    "57094b4f9928a814004714f6": "ONdigital",
+    "57094b4f9928a814004714f7": "ITV Digital",
+    "57094b4f9928a814004714f8": "three",
+    "57094b4f9928a814004714f9": "Sky Three",
+    "57094b4f9928a814004714fa": "Pick TV",
+    "57094ca7efce8f15003a7dd6": "Sky+ PVR",
+    "57094ca7efce8f15003a7dd7": "September 2007",
+    "57094ca7efce8f15003a7dd8": "Sky+HD Box",
+    "57094ca7efce8f15003a7dd9": "January 2010",
+    "57094ca7efce8f15003a7dda": "Sky+HD Box",
+    "57094d489928a8140047150a": "VideoGuard",
+    "57094d489928a8140047150b": "NDS",
+    "57094d489928a8140047150c": "Cisco Systems",
+    "57094d489928a8140047150d": "BSkyB",
+    "57094d489928a8140047150e": "Sky+",
+    "570953a7efce8f15003a7dff": "basic channels",
+    "570953a7efce8f15003a7dfe": "2007",
+    "570953a7efce8f15003a7e00": "substantially increased the asking price",
+    "570953a7efce8f15003a7e01": "Video On Demand content",
+    "570953a7efce8f15003a7e02": "HD channels",
+    "570960cf200fba1400367f01": "July 2013",
+    "570960cf200fba1400367f02": "2013",
+    "570960cf200fba1400367f03": "OneDrive",
+    "570960cf200fba1400367f04": "OneDrive for Business",
+    "570960cf200fba1400367f05": "cloud storage",
+    "570961aa200fba1400367f15": "Sam Chisholm and Rupert Murdoch",
+    "570961aa200fba1400367f16": "Astra",
+    "570961aa200fba1400367f17": "27 September 2001",
+    "570961aa200fba1400367f18": "Sky Digital",
+    "570961aa200fba1400367f19": "3.5 million",
+    "570963a5200fba1400367f33": "British Sky Broadcasting",
+    "570963a5200fba1400367f34": "telecommunications",
+    "570963a5200fba1400367f35": "11 million",
+    "570963a5200fba1400367f36": "Freeview",
+    "57096505ed30961900e84082": "Sky Q Hub",
+    "57096505ed30961900e84083": "Sky Q Silver set top boxes",
+    "57096505ed30961900e84084": "share recordings and other media",
+    "57096505ed30961900e84085": "2016",
+    "57096505ed30961900e84086": "2016",
+    "570966e0200fba1400367f4f": "MPEG-2",
+    "570966e0200fba1400367f50": "Dolby Digital",
+    "570966e0200fba1400367f51": "MPEG-4",
+    "570966e0200fba1400367f52": "OpenTV",
+    "570966e0200fba1400367f53": "DVB-S2",
+    "570967c4ed30961900e840ba": "1998",
+    "570967c4ed30961900e840bb": "Astra 2A",
+    "570967c4ed30961900e840bc": "Eurobird 1",
+    "570967c4ed30961900e840bd": "hundreds",
+    "570967c4ed30961900e840be": "28.5\u00b0E",
+    "5709686c200fba1400367f77": "22 May 2006",
+    "5709686c200fba1400367f78": "40,000",
+    "5709686c200fba1400367f79": "Thomson",
+    "5709686c200fba1400367f7a": "17,000",
+    "5709686c200fba1400367f7b": "4,222,000",
+    "57096b66200fba1400367fa7": "8 February 2007",
+    "57096b66200fba1400367fa8": "March",
+    "57096b66200fba1400367fa9": "digital terrestrial",
+    "57096b66200fba1400367faa": "Virgin Media",
+    "57096b66200fba1400367fab": "English Premier League Football",
+    "57096c95200fba1400367fbb": "free-to-view",
+    "57096c95200fba1400367fbc": "a monthly subscription",
+    "57096c95200fba1400367fbd": "VideoGuard UK",
+    "57096c95200fba1400367fbe": "Ku band LNB",
+    "57096c95200fba1400367fbf": "Sky",
+    "57096e1ced30961900e84102": "autumn of 1991",
+    "57096e1ced30961900e84103": "ITV",
+    "57096e1ced30961900e84104": "\u00a334m per year",
+    "57096e1ced30961900e84105": "The BBC",
+    "57096e1ced30961900e84106": "\u00a3304m",
+    "57096f37200fba1400367fe5": "Ofcom",
+    "57096f37200fba1400367fe6": "\u00a315\u2013100,000",
+    "57096f37200fba1400367fe7": "no",
+    "57096f37200fba1400367fe8": "not",
+    "57096f37200fba1400367fe9": "not",
+    "57097051ed30961900e84132": "1 October 1998",
+    "57097051ed30961900e84133": "Sky Digital",
+    "57097051ed30961900e84134": "Open",
+    "57097051ed30961900e84135": "ONdigital (later ITV Digital) terrestrial offering and cable services",
+    "57097051ed30961900e84136": "over 100,000",
+    "57097141200fba1400367ff9": "2007",
+    "57097141200fba1400367ffa": "Virgin Media",
+    "57097141200fba1400367ffb": "Video On Demand service",
+    "57097141200fba1400367ffc": "BBC HD",
+    "57097141200fba1400367ffd": "Channel 4 HD",
+    "57097b50200fba140036804d": "10 million",
+    "57097b50200fba140036804e": "more than 25m",
+    "57097b50200fba140036804f": "August 2004",
+    "57097b50200fba1400368050": "36%",
+    "57097b50200fba1400368051": "flattened",
+    "57097c8fed30961900e841f2": "Welfare Cash Card",
+    "57097c8fed30961900e841f3": "essentials",
+    "57097c8fed30961900e841f4": "often damaging",
+    "57097c8fed30961900e841f5": "Sky TV bills",
+    "57097c8fed30961900e841f6": "a man's presence in the household",
+    "57097d63ed30961900e841fc": "\u00a330m",
+    "57097d63ed30961900e841fd": "no indication",
+    "57097d63ed30961900e841fe": "Virgin Media",
+    "57097d63ed30961900e841ff": "BSkyB",
+    "57097d63ed30961900e84200": "carriage of their respective basic channels",
+    "570d2417fed7b91900d45c3d": "highly diversified",
+    "570d2417fed7b91900d45c3e": "second",
+    "570d2417fed7b91900d45c3f": "fourth",
+    "570d2417fed7b91900d45c40": "Melbourne",
+    "570d2417fed7b91900d45c41": "Melbourne Cricket Ground",
+    "570d2556fed7b91900d45c47": "Bendigo",
+    "570d2556fed7b91900d45c48": "New South Wales",
+    "570d2556fed7b91900d45c49": "Buckland Valley near Bright",
+    "570d2556fed7b91900d45c4a": "over 1,000",
+    "570d2556fed7b91900d45c4b": "cramped and unsanitary",
+    "570d26efb3d812140066d48f": "multi-member proportional representation",
+    "570d26efb3d812140066d490": "eight",
+    "570d26efb3d812140066d491": "five",
+    "570d26efb3d812140066d492": "four years",
+    "570d26efb3d812140066d493": "November every four years",
+    "570d28bdb3d812140066d4a3": "Labor",
+    "570d28bdb3d812140066d4a4": "The Liberals",
+    "570d28bdb3d812140066d4a5": "The Nationals",
+    "570d28bdb3d812140066d4a6": "The Greens",
+    "570d28bdb3d812140066d4a7": "Labor",
+    "570d29c2b3d812140066d4bd": "61.1%",
+    "570d29c2b3d812140066d4be": "61.1%",
+    "570d29c2b3d812140066d4bf": "Buddhism",
+    "570d29c2b3d812140066d4c0": "168,637",
+    "570d29c2b3d812140066d4c1": "168,637",
+    "570d2af0fed7b91900d45c93": "south-east",
+    "570d2af0fed7b91900d45c94": "smallest",
+    "570d2af0fed7b91900d45c95": "second-most",
+    "570d2af0fed7b91900d45c96": "Melbourne",
+    "570d2af0fed7b91900d45c97": "second-largest",
+    "570d2c20fed7b91900d45ca7": "Koori",
+    "570d2c20fed7b91900d45ca8": "1788",
+    "570d2c20fed7b91900d45ca9": "Sullivan Bay",
+    "570d2c20fed7b91900d45caa": "Sullivan Bay",
+    "570d2c20fed7b91900d45cab": "1803",
+    "570d2d68fed7b91900d45cbb": "More than 26,000 square kilometres (10,000 sq mi)",
+    "570d2d68fed7b91900d45cbc": "50%",
+    "570d2d68fed7b91900d45cbd": "6,000 square kilometres",
+    "570d2d68fed7b91900d45cbe": "90%",
+    "570d2d68fed7b91900d45cbf": "270,000",
+    "570d2f5bfed7b91900d45ccf": "1855",
+    "570d2f5bfed7b91900d45cd0": "the 1855 colonial constitution",
+    "570d2f5bfed7b91900d45cd1": "Parliament of Victoria",
+    "570d2f5bfed7b91900d45cd2": "an absolute majority in both houses, a three-fifths majority in both houses, or the approval of the Victorian people in a referendum",
+    "570d2f5bfed7b91900d45cd3": "Victoria Constitution Act 1855",
+    "570d30fdfed7b91900d45ce3": "hot winds blowing from nearby semi-deserts",
+    "570d30fdfed7b91900d45ce4": "32 \u00b0C (90 \u00b0F)",
+    "570d30fdfed7b91900d45ce5": "hot winds blowing from nearby semi-deserts",
+    "570d30fdfed7b91900d45ce6": "48.8 \u00b0C",
+    "570d30fdfed7b91900d45ce7": "7 February 2009",
+    "570d32e4fed7b91900d45d01": "state or government schools",
+    "570d32e4fed7b91900d45d02": "Victoria Department of Education",
+    "570d32e4fed7b91900d45d03": "extra costs",
+    "570d32e4fed7b91900d45d04": "Roman Catholic Church",
+    "570d32e4fed7b91900d45d05": "government-set curriculum standards",
+    "570d3468b3d812140066d543": "car",
+    "570d3468b3d812140066d544": "October 2016",
+    "570d3468b3d812140066d545": "October 2016",
+    "570d3468b3d812140066d546": "October 2016",
+    "570d3468b3d812140066d547": "Ford",
+    "570d35b7b3d812140066d54d": "2,000 m (6,600 ft)",
+    "570d35b7b3d812140066d54e": "Mount Bogong",
+    "570d35b7b3d812140066d54f": "1,986 m",
+    "570d35b7b3d812140066d550": "river systems",
+    "570d35b7b3d812140066d551": "helmeted honeyeater",
+    "570d391fb3d812140066d575": "Victorian Alps in the northeast",
+    "570d391fb3d812140066d576": "Great Dividing Range",
+    "570d391fb3d812140066d577": "east-west",
+    "570d391fb3d812140066d578": "0 \u00b0C",
+    "570d391fb3d812140066d579": "\u221211.7 \u00b0C",
+    "570d4030fed7b91900d45d9f": "government",
+    "570d4030fed7b91900d45da0": "Metro Trains Melbourne",
+    "570d4030fed7b91900d45da1": "Victorian Government",
+    "570d4030fed7b91900d45da2": "freight services",
+    "570d4030fed7b91900d45da3": "electrified, passenger system",
+    "570d418dfed7b91900d45dbd": "37",
+    "570d418dfed7b91900d45dbe": "12",
+    "570d418dfed7b91900d45dbf": "Legislative Assembly",
+    "570d418dfed7b91900d45dc0": "Legislative Council",
+    "570d418dfed7b91900d45dc1": "Linda Dessau",
+    "570d4329fed7b91900d45dd7": "1 July 1851",
+    "570d4329fed7b91900d45dd8": "1851",
+    "570d4329fed7b91900d45dd9": "one of the largest gold rushes",
+    "570d4329fed7b91900d45dda": "sevenfold",
+    "570d4329fed7b91900d45ddb": "20 million ounces",
+    "570d44abb3d812140066d5fd": "1,548",
+    "570d44abb3d812140066d5fe": "489",
+    "570d44abb3d812140066d5ff": "540,800",
+    "570d44abb3d812140066d600": "63,519",
+    "570d44abb3d812140066d601": "61",
+    "570d4606b3d812140066d619": "Victoria",
+    "570d4606b3d812140066d61a": "3 million",
+    "570d4606b3d812140066d61b": "60%",
+    "570d4606b3d812140066d61c": "two-thirds",
+    "570d4606b3d812140066d61d": "Asia",
+    "570d47b8b3d812140066d62d": "1,600 mm (5 ft 3 in) broad gauge",
+    "570d47b8b3d812140066d62e": "1,435 mm (4 ft 8 1\u20442 in) standard gauge",
+    "570d47b8b3d812140066d62f": "760 mm",
+    "570d47b8b3d812140066d630": "mountainous areas",
+    "570d47b8b3d812140066d631": "five",
+    "570d4a6bfed7b91900d45e13": "1788",
+    "570d4a6bfed7b91900d45e14": "New South Wales",
+    "570d4a6bfed7b91900d45e15": "New Holland",
+    "570d4a6bfed7b91900d45e16": "Sydney",
+    "570d4c3bfed7b91900d45e31": "1854",
+    "570d4c3bfed7b91900d45e33": "British troops",
+    "570d4c3bfed7b91900d45e32": "Eureka Stockade",
+    "570d4c3bfed7b91900d45e34": "mining licence fees",
+    "570d4c3bfed7b91900d45e35": "the Colony of Victoria Act 1855",
+    "570d4e6cb3d812140066d66d": "political party or coalition with the most seats in the Legislative Assembly. The Premier is the public face of government",
+    "570d4e6cb3d812140066d66e": "cabinet",
+    "570d4e6cb3d812140066d66f": "representatives elected to either house of parliament",
+    "570d4e6cb3d812140066d670": "Daniel Andrews",
+    "570d4e6cb3d812140066d671": "representatives elected to either house of parliament",
+    "570d50a5fed7b91900d45e7d": "17%",
+    "570d50a5fed7b91900d45e7e": "17%",
+    "570d50a5fed7b91900d45e7f": "32,463",
+    "570d50a5fed7b91900d45e80": "136,000 square kilometres (52,500 sq mi) of Victorian land. This comprises more than 60%",
+    "570d50a5fed7b91900d45e81": "60%",
+    "570d529fb3d812140066d6bb": "tourism",
+    "570d529fb3d812140066d6bc": "sports tourism",
+    "570d529fb3d812140066d6bd": "Melbourne",
+    "570d529fb3d812140066d6be": "regional cities",
+    "570d529fb3d812140066d6bf": "SurfClassic",
+    "57105da9a58dae1900cd699e": "southern and central parts",
+    "57105da9a58dae1900cd699f": "one-eighth",
+    "57105da9a58dae1900cd69a0": "1562 to 1598",
+    "57105da9a58dae1900cd69a1": "Edict of Nantes",
+    "57105da9a58dae1900cd69a2": "granted the Huguenots substantial religious, political and military autonomy",
+    "57106185b654c5140001f8db": "derision",
+    "57106185b654c5140001f8dd": "Geneva",
+    "57106185b654c5140001f8dc": "Besan\u00e7on Hugues",
+    "57106185b654c5140001f8de": "Amboise plot",
+    "57106185b654c5140001f8df": "1560",
+    "57106644b654c5140001f8e5": "the Bible in vernacular languages",
+    "57106644b654c5140001f8e6": "1294",
+    "57106644b654c5140001f8e7": "Guyard de Moulin",
+    "57106644b654c5140001f8e8": "1487",
+    "57106644b654c5140001f8e9": "Paris",
+    "57106d2fb654c5140001f8ef": "villes de s\u00fbret\u00e9",
+    "57106d2fb654c5140001f8f0": "Montpellier",
+    "57106d2fb654c5140001f8f2": "1598",
+    "57106d2fb654c5140001f8f1": "1622",
+    "57106d2fb654c5140001f8f3": "1598",
+    "571077ecb654c5140001f909": "Cape of Good Hope",
+    "571077ecb654c5140001f90a": "Cape Town",
+    "571077ecb654c5140001f90b": "Maria de la Queillerie",
+    "571077ecb654c5140001f90c": "Dutch East India Company",
+    "571077ecb654c5140001f90d": "1700",
+    "57107932a58dae1900cd69ca": "1624",
+    "57107932a58dae1900cd69cb": "Jess\u00e9 de Forest",
+    "57107932a58dae1900cd69cc": "L'\u00c9glise fran\u00e7aise \u00e0 la Nouvelle-Amsterdam",
+    "57107932a58dae1900cd69cd": "L'Eglise du Saint-Esprit",
+    "57107932a58dae1900cd69ce": "Brooklyn",
+    "57107a3ea58dae1900cd69df": "Charleston",
+    "57107a3ea58dae1900cd69e1": "Edmund Bellinger",
+    "57107a3ea58dae1900cd69e2": "Pons",
+    "57107a3ea58dae1900cd69e0": "1697",
+    "57107a3ea58dae1900cd69de": "Charleston",
+    "57107c24a58dae1900cd69e8": "Stadtholder William III of Orange",
+    "57107c24a58dae1900cd69e9": "King of England",
+    "57107c24a58dae1900cd69ea": "the League of Augsburg",
+    "57107c24a58dae1900cd69eb": "Dutch Republic",
+    "57107c24a58dae1900cd69ec": "1672",
+    "57107d73b654c5140001f91d": "Edict of Fontainebleau",
+    "57107d73b654c5140001f91e": "1685",
+    "57107d73b654c5140001f91f": "Huguenots",
+    "57107d73b654c5140001f920": "500,000",
+    "57107e6ca58dae1900cd69f2": "The Catholic Church in France",
+    "57107e6ca58dae1900cd69f3": "St. Bartholomew's Day massacre",
+    "57107e6ca58dae1900cd69f4": "5,000 to 30,000",
+    "57107e6ca58dae1900cd69f5": "their own militia",
+    "57107e6ca58dae1900cd69f6": "political reasons",
+    "57108073b654c5140001f927": "between 1621 and 1629",
+    "57108073b654c5140001f926": "southwestern France",
+    "57108073b654c5140001f928": "Louis XIII",
+    "57108073b654c5140001f929": "Louis XIII",
+    "57108073b654c5140001f925": "Huguenot rebellions",
+    "57108198b654c5140001f937": "one million",
+    "57108198b654c5140001f938": "2%",
+    "57108198b654c5140001f939": "Alsace",
+    "57108198b654c5140001f93a": "C\u00e9vennes mountain region",
+    "57108198b654c5140001f93b": "France",
+    "57108c95b654c5140001f979": "New Rochelle",
+    "57108c95b654c5140001f97a": "New Paltz",
+    "57108c95b654c5140001f97b": "Huguenot Street Historic District",
+    "57108c95b654c5140001f97c": "The \"Huguenot Street Historic District",
+    "57108c95b654c5140001f97d": "New York",
+    "57108d69b654c5140001f983": "the Dutch Republic",
+    "57108d69b654c5140001f984": "75,000 to 100,000",
+    "57108d69b654c5140001f985": "ca. 2 million",
+    "57108d69b654c5140001f986": "Amsterdam and the area of West Frisia",
+    "57108d69b654c5140001f987": "Edict of Nantes",
+    "57108ee6a58dae1900cd6a1a": "Huguon",
+    "57108ee6a58dae1900cd6a1b": "Huguon",
+    "57108ee6a58dae1900cd6a1c": "le roi Huguet",
+    "57108ee6a58dae1900cd6a1d": "pr\u00e9tendus r\u00e9form\u00e9s",
+    "57108ee6a58dae1900cd6a1e": "at night",
+    "571090abb654c5140001f995": "Kent",
+    "571090abb654c5140001f996": "The Weavers",
+    "571090abb654c5140001f997": "weavers",
+    "571090abb654c5140001f998": "Sandwich, Faversham and Maidstone",
+    "571090abb654c5140001f999": "restaurant",
+    "57109180a58dae1900cd6a41": "Cork City",
+    "57109180a58dae1900cd6a40": "Dublin, Cork, Youghal and Waterford",
+    "57109180a58dae1900cd6a42": "Dublin",
+    "57109180a58dae1900cd6a43": "a High Sheriff and one of the founders of the Bank of Ireland",
+    "57109180a58dae1900cd6a44": "1696",
+    "57109275b654c5140001f99f": "brain drain",
+    "57109275b654c5140001f9a0": "New France",
+    "57109275b654c5140001f9a1": "non-Catholics",
+    "57109275b654c5140001f9a2": "Seven Years' War",
+    "57109275b654c5140001f9a3": "1759-60",
+    "571093aba58dae1900cd6a5c": "Henry of Navarre",
+    "571093aba58dae1900cd6a5d": "1598",
+    "571093aba58dae1900cd6a5e": "protected Catholic interests",
+    "571093aba58dae1900cd6a5f": "founding of new Protestant churches",
+    "571093aba58dae1900cd6a60": "Roman Catholicism",
+    "571094b7a58dae1900cd6a66": "education",
+    "571094b7a58dae1900cd6a67": "prohibited emigration",
+    "571094b7a58dae1900cd6a68": "Four thousand",
+    "571094b7a58dae1900cd6a69": "new converts",
+    "571094b7a58dae1900cd6a6a": "New York and Virginia",
+    "571095a8a58dae1900cd6a76": "Switzerland and the Netherlands",
+    "571095a8a58dae1900cd6a77": "1555",
+    "571095a8a58dae1900cd6a78": "Fort Coligny",
+    "571095a8a58dae1900cd6a79": "1560",
+    "571095a8a58dae1900cd6a7a": "the Guanabara Confession of Faith",
+    "5710968aa58dae1900cd6a86": "Afrikaans",
+    "5710968aa58dae1900cd6a87": "wine",
+    "5710968aa58dae1900cd6a88": "Western Cape province",
+    "5710968aa58dae1900cd6a89": "surnames",
+    "571097baa58dae1900cd6a98": "Paul Revere",
+    "571097baa58dae1900cd6a99": "Henry Laurens",
+    "571097baa58dae1900cd6a9a": "Charleston",
+    "571097baa58dae1900cd6a9b": "Manakin Episcopal Church",
+    "571097baa58dae1900cd6a9c": "Texas",
+    "571098f8a58dae1900cd6ab2": "British lace industry",
+    "571098f8a58dae1900cd6ab3": "Bucks Point",
+    "571098f8a58dae1900cd6ab4": "The only reference to immigrant lacemakers in this period is of twenty-five widows who settled in Dover",
+    "571098f8a58dae1900cd6ab5": "first half of the eighteenth century",
+    "571099b2b654c5140001f9b3": "Dorotheenstadt and Friedrichstadt",
+    "571099b2b654c5140001f9b4": "one-fifth",
+    "571099b2b654c5140001f9b5": "protest against the occupation of Prussia by Napoleon",
+    "571099b2b654c5140001f9b6": "1806-07",
+    "571099b2b654c5140001f9b7": "Berlin, Stockholm, Hamburg, Frankfurt, Helsinki, and Emden",
+    "5710e8c8a58dae1900cd6b26": "Prussia",
+    "5710e8c8a58dae1900cd6b27": "C\u00e9vennes region",
+    "5710e8c8a58dae1900cd6b28": "Camisards",
+    "5710e8c8a58dae1900cd6b29": "the Catholic Church",
+    "5710e8c8a58dae1900cd6b2a": "1702 and 1709",
+    "5710e9f8a58dae1900cd6b30": "Jacksonville",
+    "5710e9f8a58dae1900cd6b31": "Jean Ribault",
+    "5710e9f8a58dae1900cd6b32": "Fort Caroline",
+    "5710e9f8a58dae1900cd6b33": "French",
+    "5710e9f8a58dae1900cd6b34": "September 1565",
+    "5710eb6fb654c5140001fa17": "Charlesfort",
+    "5710eb6fb654c5140001fa18": "Fort Caroline",
+    "5710eb6fb654c5140001fa19": "Pedro Men\u00e9ndez de Avil\u00e9s",
+    "5710eb6fb654c5140001fa1a": "1562",
+    "5710eb6fb654c5140001fa1b": "Wars of Religion",
+    "5710eca0a58dae1900cd6b3a": "Virginia",
+    "5710eca0a58dae1900cd6b3b": "Lower Norfolk County",
+    "5710eca0a58dae1900cd6b3c": "Manakin Town",
+    "5710eca0a58dae1900cd6b3d": "390",
+    "5710eca0a58dae1900cd6b3e": "12 May 1705",
+    "5710ed7bb654c5140001fa2b": "1568\u20131609",
+    "5710ed7bb654c5140001fa2c": "Spain",
+    "5710ed7bb654c5140001fa2d": "Apologie\" of William the Silent",
+    "5710ed7bb654c5140001fa2e": "William the Silent",
+    "5710ed7bb654c5140001fa2f": "Calvinist",
+    "5710f114a58dae1900cd6b60": "Foreign Protestants Naturalization Act",
+    "5710f114a58dae1900cd6b61": "1708",
+    "5710f114a58dae1900cd6b62": "50,000",
+    "5710f114a58dae1900cd6b63": "Andrew Lortie",
+    "5710f114a58dae1900cd6b64": "Pope",
+    "5710f2e2a58dae1900cd6b72": "Williamite war",
+    "5710f2e2a58dae1900cd6b73": "William of Orange",
+    "5710f2e2a58dae1900cd6b74": "Dublin, Cork, Portarlington, Lisburn, Waterford and Youghal",
+    "5710f2e2a58dae1900cd6b75": "flax",
+    "5710f2e2a58dae1900cd6b76": "linen",
+    "5710f4b8b654c5140001fa45": "Prince Louis de Cond\u00e9",
+    "5710f4b8b654c5140001fa46": "Count Ludwig von Nassau-Saarbr\u00fccken",
+    "5710f4b8b654c5140001fa47": "glass-making works",
+    "5710f4b8b654c5140001fa48": "1890s",
+    "5710f4b8b654c5140001fa49": "1604",
+    "5711119cb654c5140001fae3": "the Duchy of Prussia, the Channel Islands, and Ireland",
+    "5711119cb654c5140001fae4": "Protestant",
+    "5711119cb654c5140001fae6": "Quebec",
+    "5711119cb654c5140001fae5": "Dutch Cape Colony",
+    "5711119cb654c5140001fae7": "they were accepted and allowed to worship freely",
+    "571112ada58dae1900cd6bcc": "Hugues Capet",
+    "571112ada58dae1900cd6bcd": "Hugues hypothesis",
+    "571112ada58dae1900cd6bce": "Janet Gray",
+    "571112ada58dae1900cd6bcf": "little Hugos",
+    "571112ada58dae1900cd6bd0": "double or triple non-French linguistic origins",
+    "57111380a58dae1900cd6bd6": "Jacques Lefevre",
+    "57111380a58dae1900cd6bd7": "University of Paris",
+    "57111380a58dae1900cd6bd8": "1530",
+    "57111380a58dae1900cd6bd9": "William Farel",
+    "57111380a58dae1900cd6bda": "Jean Cauvin",
+    "57111428b654c5140001faff": "24 August \u2013 3 October 1572",
+    "57111428b654c5140001fb00": "Catholics",
+    "57111428b654c5140001fb01": "Nearly 3,000",
+    "57111428b654c5140001fb02": "1573",
+    "57111429b654c5140001fb03": "25,000",
+    "571114cfb654c5140001fb09": "Louis XIV",
+    "571114cfb654c5140001fb0a": "increasingly aggressively to force the Huguenots to convert",
+    "571114cfb654c5140001fb0b": "sent missionaries",
+    "571114cfb654c5140001fb0c": "closed Huguenot schools and excluded them from favored professions",
+    "571114cfb654c5140001fb0d": "dragonnades",
+    "5711163bb654c5140001fb13": "Westchester",
+    "5711163bb654c5140001fb14": "Bauffet's Point\" after traveling from England",
+    "5711163bb654c5140001fb15": "John Pell, Lord of Pelham Manor",
+    "5711163bb654c5140001fb16": "La Rochelle",
+    "5711163bb654c5140001fb17": "Trinity-St. Paul's Episcopal Church",
+    "57111713a58dae1900cd6c00": "quickly",
+    "57111713a58dae1900cd6c01": "merchants and artisans",
+    "57111713a58dae1900cd6c02": "E.I. du Pont",
+    "57111713a58dae1900cd6c03": "well into the nineteenth century",
+    "57111713a58dae1900cd6c04": "Eleutherian gunpowder mills",
+    "571117d4a58dae1900cd6c0a": "Pierre Bayle",
+    "571117d4a58dae1900cd6c0b": "Rotterdam",
+    "571117d4a58dae1900cd6c0c": "Historical and Critical Dictionary",
+    "571117d4a58dae1900cd6c0d": "US Library of Congress",
+    "571117d4a58dae1900cd6c0e": "Nicolas",
+    "57111992b654c5140001fb43": "The French Protestant Church of London",
+    "57111992b654c5140001fb44": "1550",
+    "57111992b654c5140001fb45": "Soho Square",
+    "57111992b654c5140001fb46": "Shoreditch",
+    "57111992b654c5140001fb47": "1724",
+    "57111ab8a58dae1900cd6c3c": "Lutheran and Reformed",
+    "57111ab8a58dae1900cd6c3d": "Germany and Scandinavia",
+    "57111ab8a58dae1900cd6c3e": "Edict of Potsdam",
+    "57111ab8a58dae1900cd6c3f": "Elector of Brandenburg and Duke of Prussia",
+    "57111ab8a58dae1900cd6c40": "The Huguenots furnished two new regiments of his army",
+    "57111b95a58dae1900cd6c50": "Frederick William, Elector of Brandenburg",
+    "57111b95a58dae1900cd6c51": "Theodor Fontane",
+    "57111b95a58dae1900cd6c52": "Adolf Galland",
+    "57111b95a58dae1900cd6c53": "Lothar de Maizi\u00e8re",
+    "57111b95a58dae1900cd6c54": "Minister of the Interior",
+    "57112686b654c5140001fbd3": "solar power",
+    "57112686b654c5140001fbd4": "Rankine cycle",
+    "57112686b654c5140001fbd5": "steam",
+    "57112686b654c5140001fbd6": "high pressure",
+    "57112686b654c5140001fbd7": "external combustion engines",
+    "571126dfa58dae1900cd6cb2": "the atmospheric engine",
+    "571126dfa58dae1900cd6cb3": "Thomas Newcomen",
+    "571126dfa58dae1900cd6cb4": "1712",
+    "571126dfa58dae1900cd6cb5": "steam pump",
+    "571126dfa58dae1900cd6cb6": "Papin",
+    "571127a5a58dae1900cd6cc4": "United Kingdom",
+    "571127a5a58dae1900cd6cc5": "21 February 1804",
+    "571127a5a58dae1900cd6cc6": "Abercynon in south Wales",
+    "571127a5a58dae1900cd6cc7": "south Wales",
+    "571127a5a58dae1900cd6cc8": "south",
+    "571135b8a58dae1900cd6d0e": "water pump",
+    "571135b8a58dae1900cd6d0f": "multi-stage centrifugal pumps",
+    "571135b8a58dae1900cd6d10": "1850s",
+    "571135b8a58dae1900cd6d11": "steam locomotives",
+    "571135b8a58dae1900cd6d12": "lower-pressure boiler feed water",
+    "57113639a58dae1900cd6d18": "three or four",
+    "57113639a58dae1900cd6d19": "triple and quadruple expansion engines",
+    "57113639a58dae1900cd6d1a": "late 19th",
+    "57113639a58dae1900cd6d1b": "marine triple expansion engines",
+    "57113639a58dae1900cd6d1c": "Olympic class",
+    "571136b8a58dae1900cd6d22": "Corliss",
+    "571136b8a58dae1900cd6d23": "Joy",
+    "571136b8a58dae1900cd6d24": "lengthening rubbing surfaces of the valve",
+    "57113ba6b654c5140001fc1f": "Lead fusible plugs",
+    "57113ba6b654c5140001fc20": "the lead melts",
+    "57113ba6b654c5140001fc21": "the steam escapes",
+    "57113ba6b654c5140001fc22": "manually suppress the fire",
+    "57113ba6b654c5140001fc23": "dampening the fire",
+    "57113be3a58dae1900cd6d28": "James Watt",
+    "57113be3a58dae1900cd6d29": "rotary",
+    "57113be3a58dae1900cd6d2a": "ten",
+    "57113be3a58dae1900cd6d2b": "1883",
+    "57113be3a58dae1900cd6d2c": "Industrial Revolution",
+    "57113c6da58dae1900cd6d32": "first century AD",
+    "57113c6da58dae1900cd6d33": "Hero of Alexandria",
+    "57113c6da58dae1900cd6d34": "Greek",
+    "57113c6da58dae1900cd6d35": "Giovanni Branca",
+    "57113c6da58dae1900cd6d36": "1606",
+    "57113f83b654c5140001fc29": "compound engines",
+    "57113f83b654c5140001fc2a": "expansions",
+    "57113f83b654c5140001fc2b": "shipping",
+    "57113f83b654c5140001fc2d": "internal combustion engines",
+    "57113f83b654c5140001fc2c": "coal",
+    "571142b3a58dae1900cd6d5a": "steam turbines",
+    "571142b3a58dae1900cd6d5b": "late",
+    "571142b3a58dae1900cd6d5c": "several hundred horsepower",
+    "571142b3a58dae1900cd6d5d": "90%",
+    "571142b3a58dae1900cd6d5e": "electric power",
+    "571144d1a58dae1900cd6d6e": "burning combustible materials",
+    "571144d1a58dae1900cd6d6f": "combustion chamber",
+    "571144d1a58dae1900cd6d70": "solar energy",
+    "571144d1a58dae1900cd6d71": "electric",
+    "57114667a58dae1900cd6d80": "steam engine indicator",
+    "57114667a58dae1900cd6d81": "1851",
+    "57114667a58dae1900cd6d82": "Charles Porter",
+    "57114667a58dae1900cd6d83": "Charles Richard",
+    "57114667a58dae1900cd6d84": "London Exhibition",
+    "5711475ca58dae1900cd6d8a": "90\u00b0",
+    "5711475ca58dae1900cd6d8b": "180\u00b0",
+    "5711475ca58dae1900cd6d8c": "90\u00b0",
+    "5711488ab654c5140001fc3d": "counterflow",
+    "5711488ab654c5140001fc3e": "two",
+    "5711488ab654c5140001fc3f": "one",
+    "5711488ab654c5140001fc40": "four",
+    "5711488ab654c5140001fc41": "expansion",
+    "57114aceb654c5140001fc47": "The Quasiturbine",
+    "57114aceb654c5140001fc48": "counterflow cycle",
+    "57114aceb654c5140001fc49": "an additional port",
+    "57114b1a2419e31400955575": "oscillating cylinder steam engine",
+    "57114b1a2419e31400955576": "trunnion",
+    "57114b1a2419e31400955577": "models",
+    "57114b1a2419e31400955578": "ships",
+    "57114dfb50c2381900b54a53": "recycled continuously",
+    "57114dfb50c2381900b54a54": "open loop",
+    "57114dfb50c2381900b54a55": "Mercury",
+    "57114dfb50c2381900b54a56": "water",
+    "57114e8d50c2381900b54a5b": "working fluid",
+    "57114e8d50c2381900b54a5c": "565 \u00b0C",
+    "57114e8d50c2381900b54a5d": "stainless steel",
+    "57114e8d50c2381900b54a5f": "63%",
+    "57114e8d50c2381900b54a5e": "30 \u00b0C",
+    "57114f0050c2381900b54a65": "Steam engines",
+    "57114f0050c2381900b54a66": "steamboats",
+    "57114f0050c2381900b54a67": "Stanley Steamer",
+    "57114f0050c2381900b54a68": "factories",
+    "57114f0050c2381900b54a69": "an increase in the land available for cultivation",
+    "571153422419e3140095557d": "Catch Me Who Can",
+    "571153422419e3140095557e": "Matthew Murray",
+    "571153422419e3140095557f": "twin-cylinder",
+    "571153422419e31400955580": "rack and pinion Middleton Railway",
+    "571153422419e31400955581": "Stockton and Darlington Railway",
+    "5711541350c2381900b54a6f": "Arthur Woolf",
+    "5711541350c2381900b54a70": "British",
+    "5711541350c2381900b54a71": "torque variability",
+    "5711541350c2381900b54a72": "cylinder volume",
+    "571154c72419e31400955587": "90%",
+    "571154c72419e31400955588": "reciprocating steam engines. In recent decades, reciprocating Diesel engines",
+    "571154c72419e31400955589": "gas turbines",
+    "571154c72419e3140095558a": "steam turbines",
+    "571154c72419e3140095558b": "reduction",
+    "571155ae2419e31400955591": "The Rankine cycle",
+    "571155ae2419e31400955592": "removed in a condenser",
+    "571155ae2419e31400955593": "1990s",
+    "571155ae2419e31400955594": "biomass",
+    "571155ae2419e31400955595": "Scottish",
+    "571156152419e3140095559b": "duty",
+    "571156152419e3140095559f": "17",
+    "571156152419e3140095559e": "the number of foot-pounds of work delivered by burning one bushel (94 pounds) of coal",
+    "571156152419e3140095559d": "94",
+    "571156152419e3140095559c": "Watt",
+    "57115ac550c2381900b54a77": "steam turbines",
+    "57115ac550c2381900b54a78": "Reciprocating piston type",
+    "57115ac550c2381900b54a79": "turbine",
+    "57115ac550c2381900b54a7a": "internal combustion engines",
+    "57115b2850c2381900b54a7f": "Thomas Savery",
+    "57115b2850c2381900b54a80": "a water pump",
+    "57115b2850c2381900b54a81": "1698",
+    "57115b2850c2381900b54a82": "Bento de Moura Portugal",
+    "57115b2850c2381900b54a83": "John Smeaton",
+    "57115b8b50c2381900b54a89": "Richard Trevithick",
+    "57115b8b50c2381900b54a8a": "Richard Trevithick and, separately, Oliver Evans",
+    "57115b8b50c2381900b54a8b": "1802",
+    "57115b8b50c2381900b54a8c": "transport",
+    "57115b8b50c2381900b54a8d": "high-pressure steam",
+    "57115bf350c2381900b54a93": "Energiprojekt AB",
+    "57115bf350c2381900b54a94": "Sweden",
+    "57115bf350c2381900b54a95": "5",
+    "57115bf350c2381900b54a96": "8.8",
+    "57115bf350c2381900b54a97": "27-30%",
+    "57115c7450c2381900b54a9d": "surface condensers",
+    "57115c7450c2381900b54a9e": "automobile radiator",
+    "57115c7450c2381900b54a9f": "locations where water is costly",
+    "57115c7450c2381900b54aa0": "dry type cooling tower is similar to an automobile radiator and is used in locations where water is costly. Evaporative (wet",
+    "57115c7450c2381900b54aa1": "3600",
+    "57115dbe2419e314009555a5": "The centrifugal governor",
+    "57115dbe2419e314009555a6": "Boulton",
+    "57115dbe2419e314009555a7": "a flour mill",
+    "57115dbe2419e314009555a8": "cotton spinning",
+    "57115dbe2419e314009555a9": "handle smaller variations",
+    "57115e532419e314009555af": "1880",
+    "57115e532419e314009555b0": "railway locomotives",
+    "57115e532419e314009555b1": "complicated",
+    "57115e532419e314009555b2": "1930",
+    "57115e532419e314009555b3": "road engines",
+    "57115f0a50c2381900b54aa7": "shortening the cutoff",
+    "57115f0a50c2381900b54aa8": "kick back",
+    "57115f0a50c2381900b54aa9": "evacuate the cylinder",
+    "57115f0a50c2381900b54aaa": "fixed",
+    "57115f652419e314009555b9": "Jer\u00f3nimo de Ayanz y Beaumont",
+    "57115f652419e314009555ba": "Spanish",
+    "57115f652419e314009555bb": "1606",
+    "57115f652419e314009555bc": "1698",
+    "57115f652419e314009555bd": "1712",
+    "57115ff82419e314009555c3": "rotating discs",
+    "57115ff82419e314009555c4": "a drive shaft",
+    "57115ff82419e314009555c5": "static discs",
+    "57115ff82419e314009555c6": "turbine casing",
+    "57115ff82419e314009555c7": "3600 revolutions per minute",
+    "5711607f2419e314009555cd": "lower",
+    "5711607f2419e314009555ce": "electric motors",
+    "5711607f2419e314009555cf": "steam turbine plant",
+    "5711607f2419e314009555d0": "Advanced Steam movement",
+    "5711607f2419e314009555d1": "pollution",
+    "571161092419e314009555d7": "Wankel engine",
+    "571161092419e314009555d8": "cylinders and valve gear",
+    "571161092419e314009555d9": "thermal expansion",
+    "5711619950c2381900b54aaf": "1775",
+    "5711619950c2381900b54ab0": "condenser",
+    "5711619950c2381900b54ab1": "half",
+    "5711619950c2381900b54ab2": "Newcomen",
+    "5711619950c2381900b54ab3": "partial vacuum generated by condensing steam",
+    "5711623e50c2381900b54ab9": "two",
+    "5711623e50c2381900b54aba": "a plug valve",
+    "5711623e50c2381900b54abb": "adjustable spring-loaded valve",
+    "5711623e50c2381900b54abc": "seal",
+    "5711623e50c2381900b54abd": "more power from the engine",
+    "5711628a2419e314009555dd": "Corliss steam engine",
+    "5711628a2419e314009555de": "1849",
+    "5711628a2419e314009555e1": "30%",
+    "5711628a2419e314009555df": "four",
+    "5711628a2419e314009555e0": "Rumford medal",
+    "571163172419e314009555e7": "thermodynamic theory",
+    "571163172419e314009555e8": "Watt",
+    "571163172419e314009555e9": "condenser",
+    "571163172419e314009555ea": "Joseph Black",
+    "571163172419e314009555eb": "latent heat",
+    "5711648850c2381900b54ac3": "relatively little work is required to drive the pump",
+    "5711648850c2381900b54ac4": "liquid phase",
+    "5711648850c2381900b54ac5": "1% to 3%",
+    "5711648850c2381900b54ac6": "1500 \u00b0C",
+    "5711651050c2381900b54acb": "injector",
+    "5711651050c2381900b54acc": "recover the latent heat of vaporisation",
+    "5711651050c2381900b54acd": "superheaters",
+    "5711651050c2381900b54ace": "bunker",
+    "5711651050c2381900b54acf": "a chain or screw stoking mechanism",
+    "5711658e50c2381900b54ad5": "feed water",
+    "5711658e50c2381900b54ad6": "British",
+    "5711658e50c2381900b54ad7": "dreadnought battleships",
+    "5711658e50c2381900b54ad8": "dreadnought battleships, and ocean liners",
+    "5711658e50c2381900b54ad9": "1905",
+    "571166352419e314009555f1": "water",
+    "571166352419e314009555f2": "a turbine connected to an electrical generator",
+    "571166352419e314009555f3": "an electrical generator",
+    "571166352419e314009555f4": "turbo-electric transmission",
+    "571166352419e314009555f5": "Sweden",
+    "5711669550c2381900b54adf": "Carnot cycle",
+    "5711669550c2381900b54ae0": "the condenser",
+    "5711669550c2381900b54ae1": "constant pressure",
+    "5711669550c2381900b54ae2": "isothermal",
+    "5711669550c2381900b54ae3": "liquid not as a gas",
+    "571a484210f8ca1400304fbd": "8",
+    "571a484210f8ca1400304fbe": "oxygen is the third-most abundant element in the universe, after hydrogen and helium",
+    "571a484210f8ca1400304fc0": "two",
+    "571a484210f8ca1400304fc1": "almost half",
+    "571a484210f8ca1400304fbf": "Diatomic oxygen gas",
+    "571bb2269499d21900609ca9": "20.8%",
+    "571bb2269499d21900609caa": "silicon dioxide",
+    "571bb2269499d21900609cab": "8",
+    "571bb2269499d21900609cad": "global downward trend",
+    "571bb2269499d21900609cac": "hydrogen and helium",
+    "571c3a685efbb31900334db2": "8",
+    "571c3a685efbb31900334db3": "chalcogen",
+    "571c3a685efbb31900334db4": "oxides",
+    "571c3a685efbb31900334db5": "third-most abundant",
+    "571c3a685efbb31900334db6": "dioxygen",
+    "571a49de4faf5e1900b8a94a": "photosynthesis",
+    "571a49de4faf5e1900b8a94b": "sunlight",
+    "571a49de4faf5e1900b8a94c": "high-altitude ozone layer",
+    "571c3c47dd7acb1400e4c09c": "oxygen",
+    "571c3c47dd7acb1400e4c09d": "water",
+    "571c3c47dd7acb1400e4c09e": "photosynthesis",
+    "571c3c47dd7acb1400e4c09f": "sunlight",
+    "571c3c47dd7acb1400e4c0a0": "ozone",
+    "571a4b0f10f8ca1400304fd5": "Robert Boyle",
+    "571a4b0f10f8ca1400304fd6": "John Mayow",
+    "571a4b0f10f8ca1400304fd7": "nitroaereus",
+    "571a4b0f10f8ca1400304fd8": "1679",
+    "571c3e8cdd7acb1400e4c0a6": "Robert Boyle",
+    "571c3e8cdd7acb1400e4c0a7": "spiritus nitroaereus",
+    "571c3e8cdd7acb1400e4c0a8": "17th",
+    "571c3e8cdd7acb1400e4c0a9": "respiration",
+    "571c3e8cdd7acb1400e4c0aa": "John Mayow",
+    "571a4d1a4faf5e1900b8a958": "Priestley",
+    "571a4d1a4faf5e1900b8a959": "clergyman",
+    "571a4d1a4faf5e1900b8a95b": "HgO",
+    "571a4d1a4faf5e1900b8a95a": "mercuric oxide",
+    "571c4132dd7acb1400e4c0b0": "mercuric oxide",
+    "571c4132dd7acb1400e4c0b1": "dephlogisticated air",
+    "571c4132dd7acb1400e4c0b2": "1775",
+    "571c4132dd7acb1400e4c0b3": "Because he published his findings first",
+    "571c4132dd7acb1400e4c0b4": "more active and lived longer",
+    "571a4ead10f8ca1400304fdd": "Leonardo da Vinci",
+    "571a4ead10f8ca1400304fde": "Philo of Byzantium",
+    "571a4ead10f8ca1400304fdf": "2nd century BCE",
+    "571a4ead10f8ca1400304fe0": "classical element fire",
+    "571c7abfdd7acb1400e4c0ba": "Philo of Byzantium",
+    "571c7abfdd7acb1400e4c0bb": "classical element fire",
+    "571c7abfdd7acb1400e4c0bc": "Pneumatica",
+    "571c7abfdd7acb1400e4c0bd": "Leonardo da Vinci",
+    "571c7abfdd7acb1400e4c0be": "air",
+    "571a50df4faf5e1900b8a960": "heat or a spark",
+    "571a50df4faf5e1900b8a961": "the source of most of the chemical energy",
+    "571a50df4faf5e1900b8a962": "Combustion hazards also apply to compounds of oxygen with a high oxidative potential",
+    "571a50df4faf5e1900b8a963": "Oxygen",
+    "571cebc05efbb31900334e49": "ignition event",
+    "571cebc05efbb31900334e4a": "oxidant",
+    "571cebc05efbb31900334e48": "rapid combustion",
+    "571cebc05efbb31900334e4b": "chemical energy",
+    "571cebc05efbb31900334e4c": "peroxides, chlorates, nitrates, perchlorates, and dichromates",
+    "571a52cb4faf5e1900b8a968": "O\n2",
+    "571a52cb4faf5e1900b8a969": "Steel pipes and storage vessels used to store and transmit both gaseous and liquid oxygen will act as a fuel",
+    "571a52cb4faf5e1900b8a96a": "1\u20443",
+    "571a52cb4faf5e1900b8a96b": "Concentrated O\n2 will allow combustion to proceed rapidly and energetically",
+    "571ce9bddd7acb1400e4c1a0": "combustion to proceed rapidly and energetically",
+    "571ce9bddd7acb1400e4c1a1": "fuel",
+    "571ce9bddd7acb1400e4c1a2": "fuel",
+    "571ce9bddd7acb1400e4c1a4": "Apollo 1 crew",
+    "571a53d410f8ca1400304fe6": "silicon",
+    "571a53d410f8ca1400304fe7": "carbon dioxide",
+    "571a53d410f8ca1400304fe5": "The Earth's mantle",
+    "571ce7f25efbb31900334e3e": "carbon dioxide",
+    "571ce7f25efbb31900334e3f": "The Earth's crustal rock",
+    "571ce7f25efbb31900334e40": "The Earth's mantle",
+    "571ce7f25efbb31900334e41": "mantle",
+    "571ce7f25efbb31900334e42": "oxygen compounds, in particular various complex silicates",
+    "571c7d55dd7acb1400e4c0c4": "monatomic",
+    "571c7d55dd7acb1400e4c0c5": "simplest",
+    "571c7d55dd7acb1400e4c0c6": "HO",
+    "571c7d55dd7acb1400e4c0c7": "hydrogen",
+    "571c7d55dd7acb1400e4c0c8": "Avogadro's law",
+    "571c8198dd7acb1400e4c0ce": "phlogiston",
+    "571c8198dd7acb1400e4c0cf": "wood or coal",
+    "571c8198dd7acb1400e4c0d0": "Air",
+    "571c8198dd7acb1400e4c0d1": "metals",
+    "571c8198dd7acb1400e4c0d2": "lighter",
+    "571c83f3dd7acb1400e4c0d8": "a covalent double bond",
+    "571c83f3dd7acb1400e4c0d9": "two",
+    "571c83f3dd7acb1400e4c0da": "Aufbau",
+    "571c83f3dd7acb1400e4c0db": "chemically bonded to each other",
+    "571c83f3dd7acb1400e4c0dc": "filling of molecular orbitals formed from the atomic orbitals of the individual oxygen atoms",
+    "571c8539dd7acb1400e4c0e2": "1773",
+    "571c8539dd7acb1400e4c0e3": "1774",
+    "571c8539dd7acb1400e4c0e4": "his work was published first",
+    "571c8539dd7acb1400e4c0e5": "Antoine Lavoisier",
+    "571c8539dd7acb1400e4c0e6": "phlogiston theory of combustion and corrosion",
+    "571c879bdd7acb1400e4c0ec": "spin triplet state",
+    "571c879bdd7acb1400e4c0ed": "triplet oxygen",
+    "571c879bdd7acb1400e4c0ee": "unpaired electrons",
+    "571c879bdd7acb1400e4c0ef": "spontaneous",
+    "571c879bdd7acb1400e4c0f0": "antibonding",
+    "571c8eb9dd7acb1400e4c0f6": "trapped air",
+    "571c8eb9dd7acb1400e4c0f7": "the same as the weight of the air that rushed back in",
+    "571c8eb9dd7acb1400e4c0f8": "increased in weight",
+    "571c8eb9dd7acb1400e4c0f9": "1777",
+    "571c8eb9dd7acb1400e4c0fa": "azote",
+    "571c9074dd7acb1400e4c100": "ozone",
+    "571c9074dd7acb1400e4c101": "allotrope",
+    "571c9074dd7acb1400e4c102": "lung tissue",
+    "571c9074dd7acb1400e4c103": "protective radiation shield",
+    "571c9074dd7acb1400e4c104": "UV",
+    "571c91c8dd7acb1400e4c10a": "dioxygen, O\n2",
+    "571c91c8dd7acb1400e4c10b": "dioxygen, O\n2",
+    "571c91c8dd7acb1400e4c10c": "allotrope",
+    "571c91c8dd7acb1400e4c10d": "energy content",
+    "571c91c8dd7acb1400e4c10e": "cellular respiration",
+    "571c9348dd7acb1400e4c114": "James Dewar",
+    "571c9348dd7acb1400e4c115": "1891",
+    "571c9348dd7acb1400e4c116": "1895",
+    "571c9348dd7acb1400e4c118": "oxyacetylene",
+    "571c96095efbb31900334dbc": "Oxygen",
+    "571c96095efbb31900334dbd": "temperature",
+    "571c96095efbb31900334dbe": "4.95 mL per liter",
+    "571c96095efbb31900334dbf": "sea water",
+    "571c96095efbb31900334dc0": "twice",
+    "571c97e2dd7acb1400e4c11e": "most abundant",
+    "571c97e2dd7acb1400e4c11f": "third",
+    "571c97e2dd7acb1400e4c120": "0.9%",
+    "571c97e2dd7acb1400e4c121": "the Earth's biosphere, air, sea and land",
+    "571c97e2dd7acb1400e4c122": "ultraviolet radiation",
+    "571caac55efbb31900334dc6": "late 19th",
+    "571caac55efbb31900334dc7": "compressing and cooling it",
+    "571caac55efbb31900334dc8": "Louis Paul Cailletet",
+    "571caac55efbb31900334dc9": "a few drops",
+    "571caac55efbb31900334dca": "March 29, 1883",
+    "571cac5d5efbb31900334dd0": "Genesis spacecraft",
+    "571cac5d5efbb31900334dd1": "oxygen-16",
+    "571cac5d5efbb31900334dd2": "Genesis",
+    "571cac5d5efbb31900334dd3": "unknown",
+    "571cac5d5efbb31900334dd4": "the Earth",
+    "571cb010dd7acb1400e4c128": "Singlet oxygen",
+    "571cb010dd7acb1400e4c129": "common organic molecules",
+    "571cb010dd7acb1400e4c12a": "photosynthesis",
+    "571cb010dd7acb1400e4c12b": "photolysis of ozone",
+    "571cb010dd7acb1400e4c12c": "Carotenoids",
+    "571cb27fdd7acb1400e4c132": "Paleoclimatologists",
+    "571cb27fdd7acb1400e4c133": "snow and rain",
+    "571cb27fdd7acb1400e4c134": "12%",
+    "571cb27fdd7acb1400e4c135": "oxygen-18",
+    "571cb27fdd7acb1400e4c136": "warmer",
+    "571cbe35dd7acb1400e4c13c": "687 and 760 nm",
+    "571cbe35dd7acb1400e4c13d": "carbon cycle",
+    "571cbe35dd7acb1400e4c13e": "vegetation canopies",
+    "571cbe35dd7acb1400e4c13f": "global scale",
+    "571cbe35dd7acb1400e4c140": "remote sensing",
+    "571cc3dedd7acb1400e4c146": "paramagnetic",
+    "571cc3dedd7acb1400e4c147": "liquid oxygen",
+    "571cc3dedd7acb1400e4c148": "unpaired electrons",
+    "571cc3dedd7acb1400e4c149": "magnetic",
+    "571cc3dedd7acb1400e4c14a": "magnet",
+    "571cc5c45efbb31900334dda": "dangerous by-products of oxygen use in organisms. Parts of the immune system of higher organisms create peroxide, superoxide, and singlet oxygen",
+    "571cc5c45efbb31900334ddb": "to destroy invading microbes",
+    "571cc5c45efbb31900334ddc": "pathogen",
+    "571cc5c45efbb31900334ddd": "anaerobic",
+    "571cc5c45efbb31900334dde": "2.5 billion years ago",
+    "571cc6f85efbb31900334de4": "90.20 K",
+    "571cc6f85efbb31900334de5": "High-purity",
+    "571cc6f85efbb31900334de6": "liquefied air",
+    "571cc6f85efbb31900334de7": "liquid nitrogen",
+    "571cc6f85efbb31900334de8": "combustible",
+    "571cc8815efbb31900334dee": "water bodies",
+    "571cc8815efbb31900334def": "lower temperatures",
+    "571cc8815efbb31900334df0": "higher oxygen content",
+    "571cc8815efbb31900334df2": "algae",
+    "571cc8815efbb31900334df1": "biochemical oxygen demand",
+    "571cca4add7acb1400e4c150": "3.5 billion years ago",
+    "571cca4add7acb1400e4c151": "Paleoproterozoic",
+    "571cca4add7acb1400e4c152": "banded iron formations",
+    "571cca4add7acb1400e4c153": "1.7 billion years ago",
+    "571cca4add7acb1400e4c154": "3\u20132.7 billion years ago",
+    "571ccc00dd7acb1400e4c15a": "oxygen cycle",
+    "571ccc00dd7acb1400e4c15b": "biogeochemical",
+    "571ccc00dd7acb1400e4c15c": "three",
+    "571ccc00dd7acb1400e4c15d": "photosynthesis",
+    "571ccc00dd7acb1400e4c15e": "oxygen",
+    "571ccd9b5efbb31900334df8": "pressure swing adsorption",
+    "571ccd9b5efbb31900334df9": "90% to 93%",
+    "571ccd9b5efbb31900334dfa": "nitrogen",
+    "571ccd9b5efbb31900334dfb": "pressure swing adsorption. Oxygen gas is increasingly obtained by these non-cryogenic",
+    "571ccd9b5efbb31900334dfc": "90% to 93%",
+    "571ccfbadd7acb1400e4c164": "water",
+    "571ccfbadd7acb1400e4c165": "Oxygen gas",
+    "571ccfbadd7acb1400e4c166": "DC",
+    "571ccfbadd7acb1400e4c167": "oxides and oxoacids",
+    "571ccfbadd7acb1400e4c168": "chemical oxygen generators or oxygen candles",
+    "571cd11add7acb1400e4c16e": "recreational use",
+    "571cd11add7acb1400e4c16f": "placebo effect",
+    "571cd11add7acb1400e4c170": "performance",
+    "571cd11add7acb1400e4c172": "placebo effect",
+    "571cd11add7acb1400e4c171": "aerobic",
+    "571cd3b55efbb31900334e03": "special oxygen chambers",
+    "571cd3b55efbb31900334e04": "carbon monoxide from the heme group of hemoglobin",
+    "571cd3b55efbb31900334e05": "anaerobic bacteria",
+    "571cd3b55efbb31900334e06": "Decompression sickness",
+    "571cd5b1dd7acb1400e4c17b": "Oxygen therapy",
+    "571cd5b1dd7acb1400e4c17a": "heart",
+    "571cd5b1dd7acb1400e4c179": "oxygen supplementation",
+    "571cd5b1dd7acb1400e4c178": "respiration",
+    "571cd5b1dd7acb1400e4c17c": "gaseous oxygen",
+    "571cd703dd7acb1400e4c182": "electronegativity",
+    "571cd703dd7acb1400e4c183": "oxides",
+    "571cd703dd7acb1400e4c184": "FeO",
+    "571cd703dd7acb1400e4c185": "chemical bonds with almost all other elements to give corresponding oxides",
+    "571cd703dd7acb1400e4c186": "corrosion",
+    "571cd88ddd7acb1400e4c18d": "cabin depressurization",
+    "571cd88ddd7acb1400e4c18e": "exothermic reaction",
+    "571cd88ddd7acb1400e4c18f": "exothermic",
+    "571cd88ddd7acb1400e4c190": "oxygen",
+    "571cda1bdd7acb1400e4c196": "high pressure",
+    "571cda1bdd7acb1400e4c198": "specially insulated tankers",
+    "571cda1bdd7acb1400e4c197": "as a liquid in specially insulated tankers",
+    "571cda1bdd7acb1400e4c199": "liquid",
+    "571cda1bdd7acb1400e4c19a": "hospitals",
+    "571cdcb85efbb31900334e0c": "organic solvents",
+    "571cdcb85efbb31900334e0d": "Epoxides",
+    "571cdcb85efbb31900334e0e": "feeder",
+    "571cdcb85efbb31900334e0f": "Epoxides",
+    "571cdcb85efbb31900334e10": "Among the most important classes of organic compounds",
+    "571cde695efbb31900334e16": "complex biomolecules",
+    "571cde695efbb31900334e17": "a few",
+    "571cde695efbb31900334e18": "carbohydrates",
+    "571cde695efbb31900334e19": "proteins",
+    "571cde695efbb31900334e1a": "bones",
+    "571ce3745efbb31900334e20": "pulmonary fibrosis",
+    "571ce3745efbb31900334e21": "permanent pulmonary fibrosis",
+    "571ce3745efbb31900334e22": "160 kPa",
+    "571ce3745efbb31900334e23": "Acute oxygen toxicity",
+    "571ce3745efbb31900334e24": "seizures",
+    "571ce5055efbb31900334e2a": "low total pressures used",
+    "571ce5055efbb31900334e2b": "30 kPa",
+    "571ce5055efbb31900334e2c": "1.4 times",
+    "571ce5055efbb31900334e2e": "no damage",
+    "571ce5055efbb31900334e2d": "marginally more than normal sea-level O\n2 partial pressure",
+    "571ce6655efbb31900334e34": "elevated partial pressures",
+    "571ce6655efbb31900334e35": "partial pressures more than 50 kilopascals (kPa)",
+    "571ce6655efbb31900334e36": "50% oxygen composition",
+    "571ce6655efbb31900334e37": "mechanical ventilators",
+    "571ce6655efbb31900334e38": "30%\u201350%",
+    "5725b33f6a3fe71400b8952d": "October 1973",
+    "5725b33f6a3fe71400b8952e": "US$3 per barrel",
+    "5725b33f6a3fe71400b8952f": "1979",
+    "5725b33f6a3fe71400b89530": "first oil shock",
+    "5725b33f6a3fe71400b89531": "Organization of Arab Petroleum Exporting Countries",
+    "5725b5a689a1e219009abd28": "to avoid being targeted by the boycott",
+    "5725b5a689a1e219009abd29": "multilateral negotiations",
+    "5725b5a689a1e219009abd2a": "January 18, 1974",
+    "5725b5a689a1e219009abd2b": "March 1974",
+    "5725b76389a1e219009abd4a": "August 15, 1971",
+    "5725b76389a1e219009abd4b": "rise and fall according to market demand",
+    "5725b76389a1e219009abd4c": "increased their reserves",
+    "5725b76389a1e219009abd4d": "September 1971",
+    "5725b76389a1e219009abd4e": "oil producers' real income decreased",
+    "5725b92e38643c19005acbd3": "less than two percent per year",
+    "5725b92e38643c19005acbd4": "1971",
+    "5725b92e38643c19005acbd5": "1973\u20131974",
+    "5725b92e38643c19005acbd6": "the oil shock",
+    "5725bad5271a42140099d0bd": "October 6, 1973",
+    "5725bad5271a42140099d0be": "Iran",
+    "5725bad5271a42140099d0bf": "ten",
+    "5725bad5271a42140099d0c0": "Iran",
+    "5725bad5271a42140099d0c1": "the price of oil] is going to rise",
+    "5725bcb6271a42140099d0eb": "response to American aid to Israel",
+    "5725bcb6271a42140099d0ec": "October 16, 1973",
+    "5725bcb6271a42140099d0ed": "the embargo, a cut in production by five percent from September's output",
+    "5725bcb6271a42140099d0ee": "$2.2 billion",
+    "5725bcb6271a42140099d0ef": "a \"principal hostile country",
+    "57261dab38643c19005ad037": "over 100 billion dollars",
+    "57261dab38643c19005ad038": "Al-Qaeda and the Taliban",
+    "57261dab38643c19005ad039": "Middle East",
+    "57261dab38643c19005ad03a": "higher oil prices",
+    "57261dab38643c19005ad03b": "Wahhabism",
+    "57261f9f271a42140099d4a9": "distribution and price disruptions",
+    "57261f9f271a42140099d4aa": "the USSR",
+    "57261f9f271a42140099d4ab": "1973",
+    "57261f9f271a42140099d4ac": "Kissinger's dominance",
+    "5726241189a1e219009ac2de": "The embargo",
+    "5726241189a1e219009ac2df": "automobiles",
+    "5726241189a1e219009ac2e0": "Macroeconomic problems",
+    "5726241189a1e219009ac2e1": "the Arctic",
+    "5726241189a1e219009ac2e2": "five to ten years",
+    "5726487b5951b619008f6edd": "Netherlands",
+    "5726487b5951b619008f6ede": "America",
+    "5726487b5951b619008f6edf": "The UK",
+    "5726487b5951b619008f6ee0": "Israelis",
+    "5726487b5951b619008f6ee1": "Ted Heath",
+    "572649d8f1498d1400e8db36": "UK",
+    "572649d8f1498d1400e8db37": "a series of strikes by coal miners and railroad workers",
+    "572649d8f1498d1400e8db38": "winter of 1973\u201374",
+    "572649d8f1498d1400e8db39": "UK, Germany, Italy, Switzerland and Norway",
+    "572649d8f1498d1400e8db3a": "Sweden",
+    "57264cac708984140094c1b3": "Price controls",
+    "57264cac708984140094c1b4": "to encourage investment",
+    "57264cac708984140094c1b5": "creating greater scarcity",
+    "57264cac708984140094c1b7": "rationing",
+    "57264d9edd62a815002e80fe": "William E. Simon",
+    "57264d9edd62a815002e80ff": "1973",
+    "57264d9edd62a815002e8100": "to coordinate the response to the embargo",
+    "57264d9edd62a815002e8101": "20%",
+    "57264efddd62a815002e8134": "55 mph",
+    "57264efddd62a815002e8135": "Emergency Highway Energy Conservation Act",
+    "57264efddd62a815002e8136": "Bill Clinton",
+    "57264efddd62a815002e8137": "November 28, 1995",
+    "57264efddd62a815002e8138": "1977",
+    "572650325951b619008f6fa9": "energy crisis",
+    "572650325951b619008f6faa": "market and technology realities",
+    "572650325951b619008f6fab": "congresses and presidents",
+    "57265200708984140094c237": "U.S.",
+    "57265200708984140094c238": "Soviet",
+    "57265200708984140094c239": "10 years",
+    "57265200708984140094c23a": "the Arabs and much of the rest of the Third World",
+    "57265360dd62a815002e819a": "Japan",
+    "57265360dd62a815002e819b": "71%",
+    "57265360dd62a815002e819c": "declared Japan a \"nonfriendly\" country to encourage it to change its noninvolvement policy",
+    "57265360dd62a815002e819d": "November 22",
+    "57265360dd62a815002e819e": "December 25",
+    "57265526708984140094c2bd": "USSR",
+    "57265526708984140094c2be": "Saudi Arabia and Iran",
+    "57265526708984140094c2bf": "Saudi",
+    "57265526708984140094c2c0": "January 1979",
+    "57265526708984140094c2c1": "November 1979",
+    "5726581fdd62a815002e823a": "Japanese imports",
+    "5726581fdd62a815002e823b": "American",
+    "5726581fdd62a815002e823c": "V8 and six cylinder",
+    "5726581fdd62a815002e823d": "Japanese",
+    "572659535951b619008f703f": "A decade",
+    "572659535951b619008f7040": "Toyota Corona Mark II",
+    "572659535951b619008f7041": "passenger space and amenities such as air conditioning, power steering, AM-FM radios, and even power windows and central locking",
+    "572659535951b619008f7042": "Acura, Lexus and Infiniti",
+    "57265a58dd62a815002e8270": "Toyota Hilux and the Datsun Truck",
+    "57265a58dd62a815002e8271": "Dodge D-50",
+    "57265a58dd62a815002e8272": "Ford, Chrysler, and GM",
+    "57265a58dd62a815002e8273": "captive import",
+    "57265bdfdd62a815002e829e": "An increase in imported cars into North America",
+    "57265bdfdd62a815002e829f": "four",
+    "57265bdfdd62a815002e82a0": "1985",
+    "57265bdfdd62a815002e82a1": "The Cadillac DeVille and Fleetwood, Buick Electra, Oldsmobile 98, Lincoln Continental, Mercury Marquis",
+    "57265bdfdd62a815002e82a2": "Chevrolet Bel Air, and Ford Galaxie 500",
+    "57265ceddd62a815002e82b8": "1979",
+    "57265ceddd62a815002e82b9": "1981",
+    "57265ceddd62a815002e82ba": "1974 Mustang I",
+    "57265e11708984140094c3bb": "1981",
+    "57265e11708984140094c3bc": "1980s",
+    "57265e11708984140094c3bd": "recover market share",
+    "57265e11708984140094c3be": "$40 per barrel",
+    "5725b41838643c19005acb7f": "Project Mercury",
+    "5725b41838643c19005acb80": "The Apollo program",
+    "5725b41838643c19005acb81": "1968",
+    "5725b41838643c19005acb82": "John F. Kennedy",
+    "5725b41838643c19005acb83": "two",
+    "5725b56589a1e219009abd20": "1961 to 1972",
+    "5725b56589a1e219009abd21": "Gemini missions",
+    "5725b56589a1e219009abd22": "the Soviet Union",
+    "5725b56589a1e219009abd23": "Skylab",
+    "5725b64d89a1e219009abd40": "1967",
+    "5725b64d89a1e219009abd41": "manned lunar landing",
+    "5725b64d89a1e219009abd42": "Budget cuts",
+    "5725b64d89a1e219009abd43": "Five",
+    "5725b64d89a1e219009abd44": "the Apollo 13 landing was prevented by an oxygen tank explosion in transit to the Moon",
+    "5725b77889a1e219009abd54": "Apollo 8",
+    "5725b77889a1e219009abd55": "Apollo 17",
+    "5725b77889a1e219009abd56": "382",
+    "5725b77889a1e219009abd57": "avionics, telecommunications, and computers",
+    "5725b888ec44d21400f3d453": "one",
+    "5725b888ec44d21400f3d454": "three",
+    "5725b888ec44d21400f3d455": "Abe Silverstein",
+    "5725b888ec44d21400f3d457": "manned lunar landings",
+    "5725b888ec44d21400f3d456": "early 1960",
+    "5725ba5038643c19005acbe7": "1960",
+    "5725ba5038643c19005acbe8": "Maxime Faget",
+    "5725ba5038643c19005acbe9": "three",
+    "5725ba5038643c19005acbea": "Hugh L. Dryden",
+    "5725bb34271a42140099d0c7": "John F. Kennedy",
+    "5725bb34271a42140099d0c8": "Soviet Union",
+    "5725bb34271a42140099d0c9": "massive financial commitment required by a manned Moon landing",
+    "5725bb34271a42140099d0ca": "James E. Webb",
+    "5725bb34271a42140099d0cb": "missile gap",
+    "5725bc7138643c19005acc1d": "Yuri Gagarin",
+    "5725bc7138643c19005acc1e": "Soviet Union",
+    "5725bc7138643c19005acc1f": "one",
+    "5725bc7138643c19005acc20": "refusing to make a commitment",
+    "5725bd4b38643c19005acc31": "April 20",
+    "5725bd4b38643c19005acc32": "Lyndon B. Johnson",
+    "5725bd4b38643c19005acc33": "one week",
+    "5725bd4b38643c19005acc34": "we are neither making maximum effort nor achieving results necessary if this country is to reach a position of leadership.\"",
+    "5725be0fec44d21400f3d4a5": "Robert R. Gilruth",
+    "5725be0fec44d21400f3d4a6": "NASA's Langley Research Center",
+    "5725be0fec44d21400f3d4a7": "Houston, Texas",
+    "5725be0fec44d21400f3d4a8": "Rice University",
+    "5725be0fec44d21400f3d4a9": "Florida",
+    "5725bf2e89a1e219009abdce": "Merritt Island",
+    "5725bf2e89a1e219009abdcf": "Kurt H. Debus",
+    "5725bf2e89a1e219009abdd0": "the LOC's first Director",
+    "5725bf2e89a1e219009abdd1": "Kennedy",
+    "5725c01389a1e219009abdd7": "three",
+    "5725c01389a1e219009abdd8": "Apollo",
+    "5725c01389a1e219009abdd9": "250,000",
+    "5725c01389a1e219009abdd6": "130 million cubic foot",
+    "5725c123271a42140099d131": "Dr. George E. Mueller",
+    "5725c123271a42140099d132": "July 23, 1963",
+    "5725c123271a42140099d133": "D. Brainerd Holmes",
+    "5725c123271a42140099d134": "Mueller",
+    "5725c2a038643c19005acc6b": "Air Force missile projects",
+    "5725c2a038643c19005acc6c": "United States Air Force",
+    "5725c2a038643c19005acc6d": "General Samuel C. Phillips",
+    "5725c2a038643c19005acc6e": "January 1964",
+    "5725c2a038643c19005acc6f": "Apollo Program Director",
+    "5725c4c289a1e219009abe44": "a rendezvous",
+    "5725c4c289a1e219009abe45": "1961",
+    "5725c4c289a1e219009abe46": "Robert Seamans",
+    "5725c604271a42140099d185": "Nicholas E. Golovin",
+    "5725c604271a42140099d186": "July 1961",
+    "5725c604271a42140099d187": "Manned Spacecraft Center",
+    "5725c604271a42140099d188": "Joseph Shea",
+    "5725c604271a42140099d189": "Manned Spacecraft Center",
+    "5725c6dcec44d21400f3d531": "Jerome Wiesner",
+    "5725c6dcec44d21400f3d532": "Golovin",
+    "5725c6dcec44d21400f3d533": "NASA",
+    "5725c6dcec44d21400f3d534": "July 11, 1962",
+    "5725c7f5271a42140099d1a1": "Wiesner",
+    "5725c7f5271a42140099d1a2": "No, that's no good",
+    "5725c7f5271a42140099d1a3": "Lunar Excursion Module",
+    "5725c7f5271a42140099d1a4": "Grumman",
+    "5725c948ec44d21400f3d577": "allowing the lander spacecraft to be used as a \"lifeboat",
+    "5725c948ec44d21400f3d578": "Apollo 13",
+    "5725c948ec44d21400f3d579": "propulsion, electrical power and life support",
+    "5725c948ec44d21400f3d57a": "1964",
+    "5725ca35271a42140099d1c1": "cone",
+    "5725ca35271a42140099d1c2": "Command/Service Module",
+    "5725ca35271a42140099d1c3": "two",
+    "5725cb4a89a1e219009abec8": "three",
+    "5725cb4a89a1e219009abec9": "lunar orbit",
+    "5725cb4a89a1e219009abeca": "ablative heat shield",
+    "5725cb4a89a1e219009abecb": "Parachutes",
+    "5725cb4a89a1e219009abecc": "5,560",
+    "5725cc2038643c19005acd1b": "A cylindrical Service Module (SM)",
+    "5725cc2038643c19005acd1c": "high-gain S-band",
+    "5725cc2038643c19005acd1d": "The Service Module was discarded",
+    "5725cc2038643c19005acd1e": "51,300",
+    "5725cc2038643c19005acd1f": "an orbital scientific instrument package",
+    "5725cda338643c19005acd3f": "North American Aviation",
+    "5725cda338643c19005acd40": "twice",
+    "5725cda338643c19005acd41": "1964",
+    "5725cda338643c19005acd42": "Saturn V",
+    "5725ceb989a1e219009abf0a": "two",
+    "5725ceb989a1e219009abf0b": "Not designed to fly through the Earth's atmosphere or return to Earth",
+    "5725ceb989a1e219009abf0d": "33,300",
+    "5725ceb989a1e219009abf0e": "over 3 days",
+    "5725d01989a1e219009abf28": "Wernher von Braun",
+    "5725d01989a1e219009abf29": "the Army",
+    "5725d01989a1e219009abf2b": "June 11, 1962",
+    "5725d0e3271a42140099d235": "dummy upper stages filled with water",
+    "5725d0e3271a42140099d236": "1964 and 1965",
+    "5725d0e3271a42140099d237": "Pegasus satellites",
+    "5725d0e3271a42140099d238": "the frequency and severity of micrometeorite impacts",
+    "5725d2ef271a42140099d255": "Saturn IB",
+    "5725d2ef271a42140099d256": "200,000 lbf (890 kN)",
+    "5725d2ef271a42140099d257": "third",
+    "5725d2ef271a42140099d258": "40,000 pounds",
+    "5725d403ec44d21400f3d65b": "The three-stage Saturn V",
+    "5725d403ec44d21400f3d65c": "33",
+    "5725d403ec44d21400f3d65d": "three",
+    "5725d403ec44d21400f3d65e": "burned liquid hydrogen",
+    "5725d51589a1e219009abf6e": "Project Mercury and Gemini veterans",
+    "5725d51589a1e219009abf6f": "two",
+    "5725d51589a1e219009abf70": "Dr. Harrison Schmitt",
+    "5725d51589a1e219009abf71": "Apollo 17",
+    "5725d51589a1e219009abf72": "lunar geology training",
+    "5725d61038643c19005acdd3": "32",
+    "5725d61038643c19005acdd4": "Distinguished Service Medal",
+    "5725d61038643c19005acdd5": "1969",
+    "5725d61038643c19005acdd6": "discipline problems with the Flight Director's orders",
+    "5725d61038643c19005acdd7": "Apollo 8",
+    "5725d6cb38643c19005acde7": "1966",
+    "5725d6cb38643c19005acde8": "265.7 nautical miles",
+    "5725d6cb38643c19005acde9": "1,142.9",
+    "5725d6cb38643c19005acdea": "Service Module engine and the Command Module heat shield",
+    "5725d79e89a1e219009abf90": "a crew would fly the first Block II CSM and LM in a dual mission known as AS-207/208, or AS-278",
+    "5725d79e89a1e219009abf91": "Apollo spacesuit",
+    "5725d79e89a1e219009abf92": "visor",
+    "5725d79e89a1e219009abf93": "water-cooled",
+    "5725d79e89a1e219009abf94": "Lunar Module Pilot",
+    "5725d8a3271a42140099d28f": "Deke Slayton",
+    "5725d8a3271a42140099d290": "Gemini and Apollo",
+    "5725d8a3271a42140099d291": "1966",
+    "5725d8a3271a42140099d292": "Deke Slayton",
+    "5725d8a3271a42140099d293": "AS-205",
+    "5725d94bec44d21400f3d69b": "canceled",
+    "5725d94bec44d21400f3d69c": "August 1967",
+    "5725d94bec44d21400f3d69d": "Saturn IB",
+    "5725d94bec44d21400f3d69e": "Apollo 1",
+    "5725da63ec44d21400f3d6ab": "Samuel Phillips",
+    "5725da63ec44d21400f3d6ac": "tiger team",
+    "5725da63ec44d21400f3d6ad": "1967",
+    "5725da63ec44d21400f3d6ae": "George Mueller",
+    "5725db4aec44d21400f3d6bd": "altitude chamber",
+    "5725db4aec44d21400f3d6be": "Grissom, White, and Chaffee",
+    "5725db4aec44d21400f3d6bf": "launch countdown",
+    "5725db4aec44d21400f3d6c0": "North American",
+    "5725dc1638643c19005ace01": "strange odor",
+    "5725dc1638643c19005ace02": "January 27, 1967",
+    "5725dc1638643c19005ace03": "sealing of the hatch",
+    "5725dc1638643c19005ace04": "asphyxiated",
+    "5725dc1638643c19005ace05": "high pressure, 100% oxygen",
+    "5725dd1689a1e219009abfe2": "both houses of Congress",
+    "5725dd1689a1e219009abfe3": "deficiencies existed in Command Module design, workmanship and quality control.\"",
+    "5725dd1689a1e219009abfe4": "George Low",
+    "5725dd1689a1e219009abfe5": "NASA immediately",
+    "5725de30ec44d21400f3d6ed": "nitrogen/oxygen",
+    "5725de30ec44d21400f3d6ee": "flammable",
+    "5725de30ec44d21400f3d6ef": "quick-release, outward opening door",
+    "5725de30ec44d21400f3d6f0": "NASA discontinued the manned Block I program",
+    "5725de30ec44d21400f3d6f1": "modified, fire-resistant Block II",
+    "5725df1838643c19005ace15": "unmanned Saturn V validation",
+    "5725df1838643c19005ace16": "Each step had to be successfully accomplished",
+    "5725df1838643c19005ace17": "letters",
+    "5725e08389a1e219009ac010": "Apollo 4 (AS-501",
+    "5725e08389a1e219009ac011": "The capability of the Command Module's heat shield to survive a trans-lunar reentry",
+    "5725e08389a1e219009ac012": "April 4, 1968",
+    "5725e08389a1e219009ac013": "a third unmanned test",
+    "5725e152271a42140099d2cd": "Apollo 5",
+    "5725e152271a42140099d2ce": "pad 37",
+    "5725e152271a42140099d2cf": "Grumman",
+    "5725e152271a42140099d2d0": "successfully test-fired and restarted, despite a computer programming error",
+    "5725e152271a42140099d2d1": "fire-in-the-hole",
+    "5725e28f38643c19005ace23": "Saturn V",
+    "5725e28f38643c19005ace24": "Zond 5",
+    "5725e28f38643c19005ace25": "Christmas Eve",
+    "5725e28f38643c19005ace26": "waste the Saturn V on another simple Earth-orbiting mission",
+    "5725e28f38643c19005ace27": "animals",
+    "5725e36f89a1e219009ac038": "Gemini",
+    "5725e36f89a1e219009ac039": "July 1969",
+    "5725e36f89a1e219009ac03a": "black-and-white television",
+    "5725e36f89a1e219009ac03b": "Neil Armstrong, Michael Collins and Buzz Aldrin",
+    "5725e36f89a1e219009ac03c": "July 24",
+    "5725e44238643c19005ace35": "Surveyor 3",
+    "5725e44238643c19005ace36": "Surveyor 3",
+    "5725e44238643c19005ace37": "removed",
+    "5725e44238643c19005ace38": "accidentally pointed into the Sun",
+    "5725e547ec44d21400f3d71f": "Lunar Roving Vehicle",
+    "5725e547ec44d21400f3d720": "Block II spacesuit",
+    "5725e547ec44d21400f3d721": "eight",
+    "5725e547ec44d21400f3d722": "over three days",
+    "5725e547ec44d21400f3d723": "mass",
+    "5725e6f6ec44d21400f3d729": "a liquid oxygen tank exploded",
+    "5725e6f6ec44d21400f3d72a": "two rookies",
+    "5725e6f6ec44d21400f3d72b": "grounded",
+    "5725e6f6ec44d21400f3d72c": "oxygen tank",
+    "5725e6f6ec44d21400f3d72d": "April 1970",
+    "5725e95f89a1e219009ac086": "Apollo 20",
+    "5725e95f89a1e219009ac087": "shrink",
+    "5725e95f89a1e219009ac088": "became museum exhibits",
+    "5725e95f89a1e219009ac089": "1971",
+    "5725ea6889a1e219009ac09e": "extremely old",
+    "5725ea6889a1e219009ac09f": "3.2 billion years",
+    "5725ea6889a1e219009ac0a0": "KREEP",
+    "5725ea6889a1e219009ac0a1": "Genesis Rock",
+    "5725eb8a38643c19005ace7f": "highly brecciated",
+    "5725eb8a38643c19005ace80": "impact process effects",
+    "5725eb8a38643c19005ace81": "materials melted near an impact crater",
+    "5725ec7538643c19005ace8f": "$170 billion",
+    "5725ec7538643c19005ace90": "15",
+    "5725ec7538643c19005ace91": "$20.4 billion",
+    "5725ee6438643c19005aceb3": "Apollo Extension Series",
+    "5725ee6438643c19005aceb4": "Apollo Applications Program",
+    "5725ee6438643c19005aceb5": "Venus",
+    "5725ef6838643c19005acece": "1973",
+    "5725ef6838643c19005acecf": "on the ground rather than in space",
+    "5725ef6838643c19005aced0": "1979",
+    "5725ef6838643c19005aced1": "Apollo Telescope Mount",
+    "5725f07f89a1e219009ac0be": "robotic Lunar Reconnaissance Orbiter",
+    "5725f07f89a1e219009ac0bf": "Apollo 11",
+    "5725f07f89a1e219009ac0c0": "retain their original colors",
+    "5725f16f89a1e219009ac0cc": "Apollo 8",
+    "5725f16f89a1e219009ac0cd": "Book of Genesis",
+    "5725f16f89a1e219009ac0ce": "one-quarter",
+    "5725f16f89a1e219009ac0cf": "troubled",
+    "5725f239271a42140099d35d": "Apollo TV",
+    "5725f239271a42140099d35e": "incompatible",
+    "5725f239271a42140099d35f": "a magnetic tape shortage",
+    "5725f239271a42140099d360": "massive numbers of magnetic tapes",
+    "5725f239271a42140099d361": "Stan Lebar",
+    "5725f39638643c19005acef7": "Nafzger",
+    "5725f39638643c19005acef8": "without destroying historical legitimacy",
+    "5725f39638643c19005acef9": "kinescope",
+    "5725f39638643c19005acefa": "Lowry Digital",
+    "5725f39638643c19005acefb": "did not include sound quality improvements",
+    "5725b7f389a1e219009abd5c": "primary law, secondary law and supplementary law",
+    "5725b7f389a1e219009abd5d": "a body of treaties and legislation",
+    "5725b7f389a1e219009abd5e": "Treaties establishing the European Union",
+    "5725b7f389a1e219009abd5f": "regulations and directives",
+    "5725b7f389a1e219009abd60": "European Parliament and the Council of the European Union",
+    "5725c28a271a42140099d14d": "a body of treaties and legislation",
+    "5725c28a271a42140099d14e": "direct effect or indirect effect",
+    "5725c28a271a42140099d14f": "primary law, secondary law and supplementary law",
+    "5725c28a271a42140099d150": "European Parliament and the Council of the European Union",
+    "57268b43dd62a815002e88f0": "primary law, secondary law and supplementary law",
+    "57268b43dd62a815002e88f1": "Treaties establishing the European Union",
+    "57268b43dd62a815002e88f2": "European Parliament and the Council of the European Union",
+    "57268b43dd62a815002e88f3": "three",
+    "5725bbec271a42140099d0d1": "courts of member states and the Court of Justice of the European Union",
+    "5725bbec271a42140099d0d2": "courts of member states",
+    "5725bbec271a42140099d0d3": "the Treaty on the Functioning of the European Union",
+    "5725bbec271a42140099d0d4": "The European Court of Justice",
+    "5725bbec271a42140099d0d5": "case law by the Court of Justice, international law and general principles of European Union law",
+    "5725c3a9ec44d21400f3d503": "the courts of member states and the Court of Justice of the European Union",
+    "5725c3a9ec44d21400f3d504": "the Treaty on the Functioning of the European Union",
+    "5725c3a9ec44d21400f3d505": "The European Court of Justice",
+    "5725c3a9ec44d21400f3d506": "case law by the Court of Justice, international law and general principles of European Union law",
+    "57268bf9dd62a815002e890a": "courts of member states and the Court of Justice of the European Union",
+    "57268bf9dd62a815002e890b": "the courts of member states",
+    "57268bf9dd62a815002e890c": "The European Court of Justice",
+    "57268bf9dd62a815002e890d": "case law by the Court of Justice, international law and general principles of European Union law",
+    "5725c743ec44d21400f3d549": "the Treaty on European Union (TEU) and the Treaty on the Functioning of the European Union (TFEU)",
+    "5725c743ec44d21400f3d54a": "The European Commission",
+    "5725c743ec44d21400f3d54b": "citizens",
+    "5725c743ec44d21400f3d54c": "The European Court of Justice",
+    "5725c743ec44d21400f3d54d": "European Council",
+    "5725ca4389a1e219009abeb4": "The TEU",
+    "5725ca4389a1e219009abeb5": "the Faroe Islands",
+    "5725ca4389a1e219009abeb6": "it cannot rule on their validity",
+    "5725ca4389a1e219009abeb7": "if the Treaty provisions have a direct effect",
+    "5725ca4389a1e219009abeb8": "as soon as they enter into force",
+    "57268d2ddd62a815002e894e": "Treaty on European Union (TEU) and the Treaty on the Functioning of the European Union (TFEU)",
+    "57268d2ddd62a815002e894f": "Gibraltar and the \u00c5land islands",
+    "57268d2ddd62a815002e8950": "as soon as they enter into force",
+    "57268d2ddd62a815002e8951": "The Court of Justice of the European Union",
+    "5725cc38ec44d21400f3d5bb": "common rules for coal and steel, and then atomic energy",
+    "5725cc38ec44d21400f3d5bc": "Treaty of Rome 1957 and the Maastricht Treaty 1992",
+    "5725cc38ec44d21400f3d5bd": "1985",
+    "5725cc38ec44d21400f3d5be": "Norway did not end up joining",
+    "5725cc38ec44d21400f3d5bf": "Greenland",
+    "57268e2bf1498d1400e8e3b0": "common rules for coal and steel, and then atomic energy",
+    "57268e2bf1498d1400e8e3b1": "1992",
+    "57268e2bf1498d1400e8e3b2": "1986",
+    "57268e2bf1498d1400e8e3b3": "1972",
+    "57268e2bf1498d1400e8e3b4": "1985",
+    "57264865dd62a815002e8062": "Following the Nice Treaty",
+    "57264865dd62a815002e8063": "France and the referendum in the Netherlands",
+    "57264865dd62a815002e8064": "very similar",
+    "57264865dd62a815002e8065": "amending treaty",
+    "57264865dd62a815002e8066": "not",
+    "57268f2bf1498d1400e8e3c4": "constitutional law of the European Union",
+    "57268f2bf1498d1400e8e3c5": "make it more transparent",
+    "57268f2bf1498d1400e8e3c6": "referendum in France and the referendum in the Netherlands",
+    "57268f2bf1498d1400e8e3c7": "the Lisbon Treaty",
+    "57264a8cdd62a815002e808c": "The European Commission",
+    "57264a8cdd62a815002e808d": "The European Commission",
+    "57264a8cdd62a815002e808e": "The Commission's President",
+    "57264a8cdd62a815002e808f": "one",
+    "57264a8cdd62a815002e8090": "Federica Mogherini",
+    "572691545951b619008f76e1": "Article 17(3",
+    "572691545951b619008f76e2": "The Commission's President",
+    "572691545951b619008f76e3": "a simple majority vote",
+    "572691545951b619008f76e4": "Ireland",
+    "572691545951b619008f76e5": "Commissioners",
+    "57264e455951b619008f6f65": "Santer Commission",
+    "57264e455951b619008f6f66": "did in fact not break any law",
+    "57264e455951b619008f6f67": "a Committee of Independent Experts",
+    "57264e455951b619008f6f68": "European Council",
+    "57264e455951b619008f6f69": "The President of the Council and a Commissioner can sit in on ECB meetings, but do not have voting rights",
+    "5726926a5951b619008f7709": "1999",
+    "5726926a5951b619008f770a": "Commission v Edith Cresson",
+    "5726926a5951b619008f770b": "a Committee of Independent Experts",
+    "5726926a5951b619008f770c": "European Anti-fraud Office",
+    "5726926a5951b619008f770d": "2012",
+    "572651f9f1498d1400e8dbee": "European Parliament and the Council of the European Union",
+    "572651f9f1498d1400e8dbef": "the elected representatives in the Parliament cannot initiate legislation against the Commission's wishes",
+    "572651f9f1498d1400e8dbf0": "every five years",
+    "572651f9f1498d1400e8dbf1": "two-thirds",
+    "572651f9f1498d1400e8dbf2": "European Parliament and the Council of the European Union",
+    "5726938af1498d1400e8e446": "the Commission",
+    "5726938af1498d1400e8e447": "European Parliament and the Council of the European Union",
+    "5726938af1498d1400e8e448": "1979",
+    "5726938af1498d1400e8e449": "every five years",
+    "5726938af1498d1400e8e44a": "European People's Party",
+    "5726545f708984140094c2a5": "different ministers of the member states",
+    "5726545f708984140094c2a6": "Donald Tusk",
+    "5726545f708984140094c2a7": "inversely to member state size",
+    "5726545f708984140094c2a8": "352",
+    "5726545f708984140094c2a9": "260",
+    "57269424dd62a815002e8a1e": "the Council",
+    "57269424dd62a815002e8a1f": "each six months",
+    "57269424dd62a815002e8a20": "352",
+    "57269424dd62a815002e8a21": "consensus",
+    "572656e4dd62a815002e81fa": "qualified majority",
+    "572656e4dd62a815002e81fb": "qualified majority",
+    "572656e4dd62a815002e81fc": "harder",
+    "572656e4dd62a815002e81fd": "TEU articles 4 and 5",
+    "572656e4dd62a815002e81fe": "Court of Justice",
+    "572695285951b619008f774b": "TFEU article 294",
+    "572695285951b619008f774c": "a majority in Parliament, a minority in the Council, and a majority in the Commission",
+    "572695285951b619008f774d": "TEU articles 4 and 5",
+    "572695285951b619008f774e": "Conciliation Committee",
+    "572658435951b619008f7025": "judicial branch",
+    "572658435951b619008f7026": "Court of Justice of the European Union",
+    "572658435951b619008f7027": "28",
+    "572658435951b619008f7028": "member state courts",
+    "572658435951b619008f7029": "ensure that in the interpretation and application of the Treaties the law is observed\"",
+    "5726965ef1498d1400e8e484": "interpreting the treaties",
+    "5726965ef1498d1400e8e485": "Court of Justice of the European Union",
+    "5726965ef1498d1400e8e486": "Civil Service Tribunal",
+    "5726965ef1498d1400e8e487": "three years",
+    "5726965ef1498d1400e8e488": "ensure that in the interpretation and application of the Treaties the law is observed\"",
+    "57265e455951b619008f70bb": "EU law has primacy",
+    "57265e455951b619008f70bc": "the Treaty conflicted with national law",
+    "57265e455951b619008f70bd": "1964 and 1968",
+    "5726975c708984140094cb1f": "European Court of Justice and the highest national courts",
+    "5726975c708984140094cb20": "1964",
+    "5726975c708984140094cb21": "the Court of Justice",
+    "572699db5951b619008f7799": "EU law",
+    "572699db5951b619008f779a": "foundational constitutional questions affecting democracy and human rights",
+    "572699db5951b619008f779b": "1972",
+    "572699db5951b619008f779d": "the ultimate authority of member states",
+    "572699db5951b619008f779c": "democracy, the rule of law and the social state principles",
+    "57269aa65951b619008f77ab": "administrative law",
+    "57269aa65951b619008f77ac": "1986",
+    "57269aa65951b619008f77ad": "All actions",
+    "57269aa65951b619008f77ae": "constitutional law",
+    "57269bb8708984140094cb95": "Van Gend en Loos v Nederlandse Administratie der Belastingen",
+    "57269bb8708984140094cb96": "article 30",
+    "57269bb8708984140094cb97": "postal",
+    "57269bb8708984140094cb98": "Treaty provisions",
+    "57269cc3dd62a815002e8b12": "Directives",
+    "57269cc3dd62a815002e8b13": "at least 4 weeks",
+    "57269cc3dd62a815002e8b14": "more than 28",
+    "57269cc3dd62a815002e8b15": "early 1990s",
+    "57269e3bf1498d1400e8e516": "the member state cannot enforce conflicting laws",
+    "57269e3bf1498d1400e8e517": "the member state cannot enforce conflicting laws",
+    "57269e3bf1498d1400e8e518": "10 years",
+    "57269e3bf1498d1400e8e519": "Swedex GmbH & Co KG",
+    "57269e3bf1498d1400e8e51a": "60",
+    "57269f3ef1498d1400e8e534": "national courts",
+    "57269f3ef1498d1400e8e535": "incorporations would only be nullified for a fixed list of reasons",
+    "57269f3ef1498d1400e8e536": "set up an insurance fund for employees to claim unpaid wages if their employers had gone insolvent",
+    "57269f3ef1498d1400e8e537": "6 million Lira",
+    "5726a00cf1498d1400e8e550": "European Court of Justice",
+    "5726a00cf1498d1400e8e551": "fundamental rights (see human rights), proportionality, legal certainty, equality before the law and subsidiarity",
+    "5726a09f708984140094cc39": "since the 1950s",
+    "5726a09f708984140094cc3a": "Article 5",
+    "5726a09f708984140094cc3b": "the least onerous",
+    "5726a14c708984140094cc51": "since the 1960s",
+    "5726a14c708984140094cc52": "public law",
+    "5726a14c708984140094cc53": "a proper legal basis",
+    "5726a14c708984140094cc54": "legal certainty and good faith",
+    "5726a1e5dd62a815002e8b86": "constitutional traditions common to the member states",
+    "5726a1e5dd62a815002e8b87": "fundamental rights recognised and protected in the constitutions of member states",
+    "5726a299dd62a815002e8b9e": "None",
+    "5726a299dd62a815002e8b9f": "member states",
+    "5726a299dd62a815002e8ba0": "1950",
+    "5726a299dd62a815002e8ba1": "European Court of Human Rights",
+    "5726a299dd62a815002e8ba2": "1999",
+    "5726a34bf1498d1400e8e59e": "2007",
+    "5726a34bf1498d1400e8e59f": "the Charter of Fundamental Rights of the European Union",
+    "5726a34bf1498d1400e8e5a0": "European Union law",
+    "5726a34bf1498d1400e8e5a1": "European Court of Justice",
+    "5726a3c6f1498d1400e8e5ae": "Treaty of Amsterdam",
+    "5726a3c6f1498d1400e8e5af": "1997",
+    "5726a3c6f1498d1400e8e5b0": "1989",
+    "5726a3c6f1498d1400e8e5b1": "30",
+    "5726a3c6f1498d1400e8e5b2": "40",
+    "5726a46cdd62a815002e8bd0": "11",
+    "5726a46cdd62a815002e8bd1": "The UK",
+    "5726a46cdd62a815002e8bd2": "Social Chapter",
+    "5726a46cdd62a815002e8bd3": "1992",
+    "5726a5525951b619008f78dd": "election of the UK Labour Party to government",
+    "5726a5525951b619008f78de": "1997",
+    "5726a5525951b619008f78df": "Works Council Directive",
+    "5726a5525951b619008f78e0": "1996",
+    "5726a5525951b619008f78e1": "workforce consultation in businesses",
+    "5726a638dd62a815002e8bf6": "France, Italy, Belgium, the Netherlands, Luxembourg and Germany",
+    "5726a638dd62a815002e8bf7": "1951",
+    "5726a638dd62a815002e8bf8": "cartels",
+    "5726a638dd62a815002e8bf9": "article 66",
+    "5726a638dd62a815002e8bfa": "1957",
+    "5726a7ecf1498d1400e8e654": "Article 101(1)",
+    "5726a7ecf1498d1400e8e655": "abuse of dominant position",
+    "5726a7ecf1498d1400e8e656": "Articles 106 and 107",
+    "5726a7ecf1498d1400e8e657": "Article 102",
+    "5726b58f5951b619008f7b55": "2007",
+    "5726b58f5951b619008f7b56": "since the Treaty of Rome 1957",
+    "5726b58f5951b619008f7b57": "consumer prices",
+    "5726b58f5951b619008f7b58": "free trade",
+    "5726b58f5951b619008f7b59": "the Court of Justice",
+    "5726b718dd62a815002e8dbe": "customs union",
+    "5726b718dd62a815002e8dbf": "parallel importers",
+    "5726b718dd62a815002e8dc0": "private actors",
+    "5726b718dd62a815002e8dc1": "Commission v France",
+    "5726b718dd62a815002e8dc2": "protest",
+    "5726b929f1498d1400e8e8e8": "25 per cent",
+    "5726b929f1498d1400e8e8e9": "France",
+    "5726b929f1498d1400e8e8ea": "2009",
+    "5726b929f1498d1400e8e8eb": "cocoa butter",
+    "5726b929f1498d1400e8e8ec": "motorcycles or mopeds pulling trailers",
+    "5726ba2c708984140094cf59": "Keck and Mithouard",
+    "5726ba2c708984140094cf5a": "prevent cut throat competition",
+    "5726ba2c708984140094cf5b": "Konsumentombudsmannen v De Agostini",
+    "5726ba2c708984140094cf5c": "Unfair Commercial Practices Directive",
+    "5726baf2dd62a815002e8e74": "people to pursue their life goals in any country through free movement",
+    "5726baf2dd62a815002e8e75": "European Community",
+    "5726baf2dd62a815002e8e76": "citizenship",
+    "5726baf2dd62a815002e8e77": "Steymann v Staatssecretaris van Justitie",
+    "5726baf2dd62a815002e8e78": "stay",
+    "5726bc1add62a815002e8ea6": "articles 1 to 7",
+    "5726bc1add62a815002e8ea7": "Jean-Marc Bosman",
+    "5726bc1add62a815002e8ea8": "Gaelic",
+    "5726bc1add62a815002e8ea9": "Hendrix v Employee Insurance Institute",
+    "5726bc1add62a815002e8eaa": "between 3 and 14",
+    "5726bcde708984140094cfbf": "Citizenship of the EU",
+    "5726bcde708984140094cfc0": "the number of social services that people can access wherever they move",
+    "5726bcde708984140094cfc1": "Commission v Austria",
+    "5726bcde708984140094cfc2": "higher education",
+    "5726c002708984140094d073": "article 49, and \"freedom to provide services\" in article 56",
+    "5726c002708984140094d074": "if they were non-discriminatory",
+    "5726c002708984140094d075": "Reyners v Belgium",
+    "5726c002708984140094d076": "TFEU article 49",
+    "5726c002708984140094d077": "Commission v Italy",
+    "5726c19add62a815002e8f8a": "2006",
+    "5726c19add62a815002e8f8b": "shipping toxic waste",
+    "5726c19add62a815002e8f8c": "October 2007",
+    "5726c19add62a815002e8f8d": "2005",
+    "5726c3da708984140094d0d9": "German health clinic",
+    "5726c3da708984140094d0da": "Dutch law said only people established in the Netherlands could give legal advice",
+    "5726c3da708984140094d0db": "narcotic drugs",
+    "5726c3da708984140094d0dc": "treatment",
+    "5726c5a9f1498d1400e8eac4": "Daily Mail",
+    "5726c5a9f1498d1400e8eac5": "\u00a31",
+    "5726c5a9f1498d1400e8eac6": "\u00a31",
+    "5726c5a9f1498d1400e8eac7": "creditor protection, labour rights to participate in work, or the public interest in collecting taxes",
+    "5726c5a9f1498d1400e8eac8": "\u00dcberseering BV v Nordic Construction GmbH",
+    "5725b81b271a42140099d097": "Amazonia or the Amazon Jungle",
+    "5725b81b271a42140099d098": "5,500,000",
+    "5725b81b271a42140099d099": "nine",
+    "5725b81b271a42140099d09a": "four",
+    "5725b81b271a42140099d09b": "over half",
+    "5728349dff5b5019007d9efe": "Amazoneregenwoud",
+    "5728349dff5b5019007d9eff": "The Amazon rainforest",
+    "5728349dff5b5019007d9f00": "Brazil",
+    "5728349dff5b5019007d9f01": "over half",
+    "5728349dff5b5019007d9f02": "16,000",
+    "5729e2316aef0514001550c4": "moist broadleaf forest",
+    "5729e2316aef0514001550c5": "7,000,000",
+    "5729e2316aef0514001550c6": "nine",
+    "5729e2316aef0514001550c7": "Brazil",
+    "5729e2316aef0514001550c8": "390 billion",
+    "5725be0f271a42140099d117": "wetter",
+    "5725be0f271a42140099d118": "Climate fluctuations",
+    "5725be0f271a42140099d119": "Oligocene",
+    "5725be0f271a42140099d11a": "last glacial maximum",
+    "5725be0f271a42140099d11b": "the rainforest still managed to thrive",
+    "57283adcff5b5019007d9f94": "the extinction of the dinosaurs",
+    "57283adcff5b5019007d9f95": "45\u00b0",
+    "57283adcff5b5019007d9f96": "Climate fluctuations",
+    "57283adcff5b5019007d9f97": "Oligocene",
+    "57283adcff5b5019007d9f98": "expanded",
+    "5729e500af94a219006aa6b5": "Cretaceous\u2013Paleogene extinction",
+    "5729e500af94a219006aa6b6": "66\u201334 Mya",
+    "5729e500af94a219006aa6b7": "Middle Miocene",
+    "5729e500af94a219006aa6b8": "last glacial maximum",
+    "5729e500af94a219006aa6b9": "34 million",
+    "5725c071271a42140099d127": "mid-Eocene",
+    "5725c071271a42140099d128": "toward the Atlantic",
+    "5725c071271a42140099d129": "Solim\u00f5es Basin",
+    "5725c071271a42140099d12a": "5\u201310 million years",
+    "5725c071271a42140099d12b": "easterly flow toward the Atlantic",
+    "57283d173acd2414000df78f": "mid-Eocene",
+    "57283d173acd2414000df790": "the Pacific",
+    "57283d173acd2414000df791": "the Pacific",
+    "57283d173acd2414000df792": "Amazonas Basin",
+    "57283d173acd2414000df793": "Solim\u00f5es Basin",
+    "5729e6313f37b319004785a9": "mid-Eocene",
+    "5729e6313f37b319004785aa": "along the middle",
+    "5729e6313f37b319004785ab": "the Atlantic",
+    "5729e6313f37b319004785ac": "the Pacific",
+    "5729e6313f37b319004785ad": "Solim\u00f5es Basin",
+    "5725c41eec44d21400f3d50b": "Last Glacial Maximum",
+    "5725c41eec44d21400f3d50c": "that rainfall in the basin during the LGM was lower than for the present",
+    "5725c41eec44d21400f3d50d": "the rainforest was reduced to small, isolated refugia separated by open forest and grassland",
+    "5725c41eec44d21400f3d50e": "difficult to resolve",
+    "5725c41eec44d21400f3d50f": "reasonably well supported by the available data",
+    "572841772ca10214002da1a6": "21,000",
+    "572841772ca10214002da1a7": "Last Glacial Maximum (LGM) and subsequent deglaciation",
+    "572841772ca10214002da1a8": "sediment deposits",
+    "572841772ca10214002da1a9": "reduced moist tropical vegetation cover",
+    "572a0bfaaf94a219006aa779": "21,000 years",
+    "572a0bfaaf94a219006aa77a": "sediment",
+    "572a0bfaaf94a219006aa77b": "moist tropical",
+    "572a0bfaaf94a219006aa77c": "open forest and grassland",
+    "572a0bfaaf94a219006aa77d": "practical limitations of working in the rainforest mean that data sampling is biased away from the center of the Amazon basin",
+    "5725c63438643c19005acc9f": "CALIPSO",
+    "5725c63438643c19005acca0": "182 million",
+    "5725c63438643c19005acca1": "1,600",
+    "5725c63438643c19005acca2": "Amazon basin",
+    "5725c63438643c19005acca3": "132 million",
+    "5728455bff5b5019007da078": "CALIPSO satellite",
+    "5728455bff5b5019007da079": "182 million tons",
+    "5728455bff5b5019007da07a": "27.7 million tons",
+    "5728455bff5b5019007da07b": "132 million tons",
+    "5728455bff5b5019007da07c": "43 million tons",
+    "5729ea263f37b319004785bd": "CALIPSO",
+    "5729ea263f37b319004785be": "NASA's CALIPSO",
+    "5729ea263f37b319004785bf": "182 million tons",
+    "5729ea263f37b319004785c0": "1,600",
+    "5729ea263f37b319004785c1": "27.7 million",
+    "5725c95f38643c19005accf3": "Amazonia: Man and Culture in a Counterfeit Paradise",
+    "5725c95f38643c19005accf4": "0.52/sq mi",
+    "5725c95f38643c19005accf5": "agriculture",
+    "5725c95f38643c19005accf6": "recent anthropological",
+    "5725c95f38643c19005accf7": "5 million",
+    "572847dd4b864d19001648bc": "poor soil",
+    "572847dd4b864d19001648bd": "Betty Meggers",
+    "572847dd4b864d19001648be": "0.2",
+    "572847dd4b864d19001648bf": "Amazonia: Man and Culture in a Counterfeit Paradise",
+    "5729eb34af94a219006aa6c9": "Betty Meggers",
+    "5729eb34af94a219006aa6ca": "Amazonia: Man and Culture in a Counterfeit Paradise",
+    "5729eb34af94a219006aa6cb": "0.2 inhabitants per square kilometre (0.52/sq mi",
+    "5729eb34af94a219006aa6cc": "5 million",
+    "5729eb34af94a219006aa6cd": "200,000",
+    "5725cbb289a1e219009abed2": "Francisco de Orellana",
+    "5725cbb289a1e219009abed3": "1540s",
+    "5725cbb289a1e219009abed4": "smallpox",
+    "5725cbb289a1e219009abed5": "between AD 0\u20131250",
+    "5725cbb289a1e219009abed6": "AD 0\u20131250",
+    "5729edd56aef051400155112": "Francisco de Orellana",
+    "5729edd56aef051400155113": "1542",
+    "5729edd56aef051400155114": "AD 0\u20131250",
+    "5729edd56aef051400155115": "Ondemar Dias",
+    "5729edd56aef051400155116": "11,000",
+    "5725cf3238643c19005acd61": "black earth",
+    "5725cf3238643c19005acd62": "large areas",
+    "5725cf3238643c19005acd63": "agriculture and silviculture",
+    "5725cf3238643c19005acd64": "Xingu",
+    "5725cf3238643c19005acd65": "Michael Heckenberger and colleagues of the University of Florida",
+    "5729ef266aef05140015511c": "Terra preta",
+    "5729ef266aef05140015511d": "agriculture and silviculture",
+    "5729ef266aef05140015511e": "Xingu",
+    "5729ef266aef05140015511f": "Michael Heckenberger",
+    "5729ef266aef051400155120": "roads, bridges and large plazas",
+    "5725d16aec44d21400f3d61b": "2.5 million",
+    "5725d16aec44d21400f3d61c": "One in five",
+    "5725d16aec44d21400f3d61d": "40,000",
+    "5725d16aec44d21400f3d61e": "2,200",
+    "5725d16aec44d21400f3d61f": "128,843",
+    "5729f0db6aef051400155126": "2.5 million",
+    "5729f0db6aef051400155127": "2,000",
+    "5729f0db6aef051400155128": "40,000",
+    "5729f0db6aef051400155129": "378",
+    "5729f0db6aef05140015512a": "One in five",
+    "5726722bdd62a815002e8528": "62",
+    "5726722bdd62a815002e8529": "16,000",
+    "5726722bdd62a815002e852a": "90,790",
+    "5726722bdd62a815002e852b": "356 \u00b1 47 tonnes per hectare",
+    "5726722bdd62a815002e852c": "438,000",
+    "5729f2646aef051400155130": "highest",
+    "5729f2646aef051400155131": "1,100",
+    "5729f2646aef051400155132": "90,790",
+    "5729f2646aef051400155133": "356 \u00b1 47 tonnes",
+    "5729f2646aef051400155134": "438,000",
+    "5729f3831d0469140077967b": "electric eels",
+    "5729f3831d0469140077967c": "black caiman",
+    "5729f3831d0469140077967d": "piranha",
+    "5729f3831d0469140077967e": "lipophilic alkaloid toxins",
+    "5729f3831d0469140077967f": "Vampire",
+    "5729fd56af94a219006aa72f": "Deforestation",
+    "5729fd56af94a219006aa730": "early 1960s",
+    "5729fd56af94a219006aa731": "slash and burn",
+    "5729fd56af94a219006aa732": "loss of soil fertility and weed invasion",
+    "5729fd56af94a219006aa733": "areas cleared of forest",
+    "5729feaf6aef051400155188": "587,000",
+    "5729feaf6aef051400155189": "587,000",
+    "5729feaf6aef05140015518a": "pasture for cattle",
+    "5729feaf6aef05140015518b": "second-largest",
+    "5729feaf6aef05140015518c": "91%",
+    "572a005f1d046914007796b7": "soy",
+    "572a005f1d046914007796b8": "increased settlement and deforestation",
+    "572a005f1d046914007796b9": "8,646",
+    "572a005f1d046914007796bb": "declined",
+    "572a005f1d046914007796ba": "18%",
+    "572a020f6aef051400155198": "biodiversity",
+    "572a020f6aef051400155199": "destruction of the forest",
+    "572a020f6aef05140015519a": "carbon",
+    "572a020f6aef05140015519b": "1.1 \u00d7 1011 metric tonnes",
+    "572a020f6aef05140015519c": "1.1 \u00d7 1011",
+    "572a03a06aef0514001551aa": "severely reduced rainfall and increased temperatures",
+    "572a03a06aef0514001551ab": "greenhouse gas",
+    "572a03a06aef0514001551ac": "2100",
+    "572a03a06aef0514001551ad": "though the 21st century",
+    "572a03a06aef0514001551ae": "climate change in addition to deforestation",
+    "572a064a3f37b3190047865d": "indigenous",
+    "572a064a3f37b3190047865e": "community-based",
+    "572a064a3f37b3190047865f": "deforestation and ecocide",
+    "572a064a3f37b31900478660": "Urarina",
+    "572a064a3f37b31900478661": "non-human primates",
+    "572a07c11d046914007796d5": "remote sensing",
+    "572a07c11d046914007796d6": "Trio Tribe",
+    "572a07c11d046914007796d7": "southern Suriname",
+    "572a07c11d046914007796d8": "map out their ancestral lands to help strengthen their territorial claims",
+    "572a07c11d046914007796d9": "to protect their tribal lands from commercial interests",
+    "572a09abaf94a219006aa75b": "tree growth stages within different parts of the forest",
+    "572a09abaf94a219006aa75c": "carbon related",
+    "572a09abaf94a219006aa75d": "Tatiana Kuplich",
+    "572a09abaf94a219006aa75e": "2006",
+    "572a09abaf94a219006aa75f": "Synthetic aperture radar",
+    "572a0b101d046914007796e9": "2005",
+    "572a0b101d046914007796eb": "Brazilian National Institute of Amazonian Research",
+    "572a0b101d046914007796ec": "deforestation",
+    "572a0b101d046914007796ed": "savanna or desert",
+    "572a0b101d046914007796ea": "Woods Hole Research Center",
+    "572a0bebaf94a219006aa76f": "2010",
+    "572a0bebaf94a219006aa770": "1,160,000",
+    "572a0bebaf94a219006aa771": "three",
+    "572a0bebaf94a219006aa772": "2005",
+    "572a0bebaf94a219006aa773": "1.5",
+    "5725b9db38643c19005acbe1": "comb jellies",
+    "5725b9db38643c19005acbe2": "marine waters worldwide",
+    "5725b9db38643c19005acbe3": "1.5 m (4 ft 11 in)",
+    "5725c0f289a1e219009abdf2": "a phylum of animals that live in marine waters worldwide",
+    "5725c0f289a1e219009abdf3": "combs\u2019 \u2013 groups of cilia",
+    "5725c0f289a1e219009abdf4": "water flow",
+    "5725c0f289a1e219009abdf5": "1.5 m (4 ft 11 in)",
+    "5725c0f289a1e219009abdf6": "the \u2018combs\u2019 \u2013 groups of cilia",
+    "57263eaa38643c19005ad371": "comb jellies",
+    "57263eaa38643c19005ad372": "1.5 m (4 ft 11 in)",
+    "57263eaa38643c19005ad373": "water flow through the body cavity",
+    "57263eaa38643c19005ad374": "comb",
+    "57263eaa38643c19005ad375": "marine waters worldwide",
+    "5725bae289a1e219009abd90": "ten times their own weight",
+    "5725bae289a1e219009abd91": "100\u2013150",
+    "5725bae289a1e219009abd92": "25",
+    "5725c337271a42140099d163": "100\u2013150",
+    "5725c337271a42140099d164": "tentilla",
+    "5725c337271a42140099d165": "ten times their own weight",
+    "5725c337271a42140099d166": "tentacles",
+    "5725c337271a42140099d167": "large, stiffened cilia",
+    "5726400589a1e219009ac5ee": "ten times their own weight",
+    "5726400589a1e219009ac5ef": "tentilla",
+    "5726400589a1e219009ac5f0": "cilia",
+    "5726400589a1e219009ac5f1": "egg-shaped bodies and a pair of retractable tentacles fringed with tentilla",
+    "5726400589a1e219009ac5f2": "100\u2013150",
+    "5725bc0338643c19005acc11": "hermaphrodites",
+    "5725bc0338643c19005acc12": "miniature cydippids",
+    "5725bc0338643c19005acc13": "juveniles",
+    "5725c57a89a1e219009abe5e": "a single animal can produce both eggs and sperm",
+    "5725c57a89a1e219009abe5f": "produce both eggs and sperm",
+    "5725c57a89a1e219009abe60": "sequential",
+    "5725c57a89a1e219009abe61": "platyctenids",
+    "5725c57a89a1e219009abe62": "The combination of hermaphroditism and early reproduction",
+    "5726415bec44d21400f3dcd1": "a single animal can produce both eggs and sperm",
+    "5726415bec44d21400f3dcd2": "can produce both eggs and sperm",
+    "5726415bec44d21400f3dcd3": "the eggs and sperm mature at different times",
+    "5726415bec44d21400f3dcd4": "platyctenids",
+    "5726415bec44d21400f3dcd5": "beroids",
+    "5725bdbe38643c19005acc39": "Black Sea",
+    "5725bdbe38643c19005acc3a": "Mnemiopsis",
+    "5725bdbe38643c19005acc3b": "over-fishing and long-term environmental changes",
+    "5725c69738643c19005accb9": "ctenophores",
+    "5725c69738643c19005accba": "Mnemiopsis",
+    "5725c69738643c19005accbb": "fish larvae",
+    "5725c69738643c19005accbc": "bays",
+    "5726431d271a42140099d7f5": "bays",
+    "5726431d271a42140099d7f6": "planktonic plants",
+    "5726431d271a42140099d7f7": "Mnemiopsis",
+    "5726431d271a42140099d7f8": "causing fish stocks to collapse",
+    "5726431d271a42140099d7f9": "over-fishing",
+    "5725c91e38643c19005acceb": "66 million years ago",
+    "5725c91e38643c19005accec": "monophyletic",
+    "5725c91e38643c19005acced": "515 million years ago",
+    "5725c91e38643c19005accee": "many more comb-rows",
+    "5726449f1125e71900ae1928": "515 million years ago",
+    "5726449f1125e71900ae1929": "Cretaceous\u2013Paleogene extinction",
+    "5726449f1125e71900ae192a": "monophyletic",
+    "5726449f1125e71900ae192b": "tentacles",
+    "5725cb33271a42140099d1db": "cnidarians",
+    "5725cb33271a42140099d1dc": "colloblasts",
+    "5725cb33271a42140099d1dd": "bilaterians",
+    "5725cb33271a42140099d1de": "Ctenophores",
+    "5725cb33271a42140099d1df": "colloblasts",
+    "572646655951b619008f6ebf": "cnidarians",
+    "572646655951b619008f6ec0": "colloblasts",
+    "572646655951b619008f6ec1": "colloblasts",
+    "572646655951b619008f6ec2": "ctenophores and cnidarians",
+    "572646655951b619008f6ec3": "bilaterians",
+    "572647d0708984140094c14b": "mesoglea",
+    "572647d0708984140094c14c": "diploblastic",
+    "572647d0708984140094c14d": "sponges and cnidarians, ctenophores",
+    "572647d0708984140094c14e": "sponges",
+    "572648e8dd62a815002e8076": "cilia",
+    "572648e8dd62a815002e8077": "locomotion",
+    "572648e8dd62a815002e8078": "ctenes",
+    "572648e8dd62a815002e8079": "comb-bearing",
+    "57264a0ef1498d1400e8db40": "cydippid Pleurobrachia",
+    "57264a0ef1498d1400e8db41": "oceanic species",
+    "57264a0ef1498d1400e8db42": "to withstand waves and swirling sediment particles",
+    "57264a0ef1498d1400e8db43": "Pleurobrachia, Beroe and Mnemiopsis",
+    "57264b1ddd62a815002e80a0": "gastrodermis",
+    "57264b1ddd62a815002e80a1": "bioluminescence",
+    "57264b1ddd62a815002e80a2": "pharynx",
+    "57264b1ddd62a815002e80a3": "internal canals",
+    "57264b1ddd62a815002e80a4": "the mouth and pharynx",
+    "57264cfa708984140094c1c3": "swimming-plates",
+    "57264cfa708984140094c1c4": "ctenes",
+    "57264cfa708984140094c1c5": "the extra compact filament is suspected to have a supporting function",
+    "57264cfa708984140094c1c6": "in the direction in which the mouth is pointing",
+    "57264cfa708984140094c1c7": "2 millimeters",
+    "57264e66dd62a815002e811a": "osmotic pressure",
+    "57264e66dd62a815002e811b": "the mesoglea",
+    "57264e66dd62a815002e811c": "reduce its volume and increase its density",
+    "57264e66dd62a815002e811d": "pump water out of the mesoglea to reduce its volume",
+    "572655e5f1498d1400e8dc5e": "aboral organ",
+    "572655e5f1498d1400e8dc5f": "at the opposite end from the mouth",
+    "572655e5f1498d1400e8dc60": "a transparent dome made of long, immobile cilia",
+    "572655e5f1498d1400e8dc61": "statocyst",
+    "572655e5f1498d1400e8dc62": "a balance sensor",
+    "57265746dd62a815002e8218": "sea gooseberry",
+    "57265746dd62a815002e8219": "a pair of long, slender tentacles",
+    "57265746dd62a815002e821a": "spherical",
+    "57265746dd62a815002e821b": "a sheath",
+    "57265746dd62a815002e821c": "narrow end",
+    "572658daf1498d1400e8dcac": "tentilla",
+    "572658daf1498d1400e8dcad": "specialized mushroom-shaped cells in the outer layer of the epidermis",
+    "572658daf1498d1400e8dcae": "they contain striated muscle",
+    "572658daf1498d1400e8dcaf": "three",
+    "572658daf1498d1400e8dcb0": "capturing prey",
+    "572659ea5951b619008f7051": "eight",
+    "572659ea5951b619008f7052": "near the mouth to the opposite end",
+    "572659ea5951b619008f7053": "evenly round the body",
+    "572659ea5951b619008f7054": "a ciliary groove",
+    "57265aaf5951b619008f706b": "lobes",
+    "57265aaf5951b619008f706c": "gelatinous projections edged with cilia that produce water currents that help direct microscopic prey toward the mouth",
+    "57265aaf5951b619008f706d": "four",
+    "57265aaf5951b619008f706e": "help direct microscopic prey toward the mouth",
+    "57265aaf5951b619008f706f": "suspended planktonic prey",
+    "57265c10f1498d1400e8dd36": "clapping their lobes",
+    "57265c10f1498d1400e8dd37": "escape from danger by clapping their lobes, so that the jet of expelled water drives them backwards very quickly",
+    "57265c10f1498d1400e8dd38": "nerves",
+    "57265c10f1498d1400e8dd39": "nerves",
+    "57265d86f1498d1400e8dd50": "Nuda",
+    "57265d86f1498d1400e8dd51": "Beroida",
+    "57265d86f1498d1400e8dd52": "zip\" the mouth shut when the animal is not feeding",
+    "57265d86f1498d1400e8dd53": "streamlines",
+    "57265d86f1498d1400e8dd54": "macrocilia",
+    "57265e97708984140094c3c3": "Cestida",
+    "57265e97708984140094c3c6": "Cestum veneris",
+    "57265e97708984140094c3c4": "belt animals",
+    "57265e97708984140094c3c5": "by undulating their bodies",
+    "57265e97708984140094c3c7": "Velamen parallelum",
+    "572681c1dd62a815002e8796": "tentilla-bearing tentacles",
+    "572681c1dd62a815002e8797": "a muscular \"foot\"",
+    "572681c1dd62a815002e8798": "comb-rows",
+    "572681c1dd62a815002e8799": "rocks, algae, or the body surfaces of other invertebrates",
+    "572683075951b619008f7513": "via pores in the epidermis",
+    "572683075951b619008f7514": "internal fertilization",
+    "572683075951b619008f7515": "Mnemiopsis",
+    "572683075951b619008f7516": "in the parts of the internal canal network under the comb rows",
+    "572683075951b619008f7517": "external",
+    "572684365951b619008f753f": "tentacles and tentacle sheaths",
+    "572684365951b619008f7540": "flat, bottom-dwelling",
+    "572684365951b619008f7541": "after dropping to the sea-floor",
+    "572684365951b619008f7542": "true larvae",
+    "572684365951b619008f7543": "Beroe",
+    "572686fc708984140094c8e5": "they produce secretions (ink) that luminesce at much the same wavelengths as their bodies",
+    "572686fc708984140094c8e6": "When some species",
+    "572686fc708984140094c8e7": "ink",
+    "572686fc708984140094c8e8": "Juveniles",
+    "5726887e708984140094c917": "parasitic",
+    "5726887e708984140094c918": "jellyfish",
+    "5726887e708984140094c919": "incorporate their prey's nematocysts (stinging cells) into their own tentacles",
+    "5726887e708984140094c91a": "smaller, weaker swimmers",
+    "5726887e708984140094c91b": "Lampea",
+    "57268a37f1498d1400e8e33c": "low ratio of organic matter to salt and water",
+    "57268a37f1498d1400e8e33d": "keta",
+    "57268a37f1498d1400e8e33e": "other ctenophores",
+    "57268a37f1498d1400e8e33f": "blooms in the Red Sea",
+    "57268a37f1498d1400e8e340": "ctenophores",
+    "57268c01dd62a815002e8912": "eating both fish larvae and small crustaceans",
+    "57268c01dd62a815002e8913": "via the ballast tanks of ships",
+    "57268c01dd62a815002e8915": "accidental introduction of the Mnemiopsis-eating North American ctenophore Beroe ovata",
+    "57268c01dd62a815002e8914": "late 1980s",
+    "57268c01dd62a815002e8916": "slowed the animal's metabolism",
+    "57268da7f1498d1400e8e39c": "soft, gelatinous bodies",
+    "57268da7f1498d1400e8e39d": "a comb jelly",
+    "57268da7f1498d1400e8e39e": "mid-Cambrian period",
+    "57268da7f1498d1400e8e39f": "Three",
+    "57268da7f1498d1400e8e3a0": "tentacles",
+    "57268f05dd62a815002e8990": "515 million years ago",
+    "57268f05dd62a815002e8991": "sessile frond-like",
+    "57268f05dd62a815002e8992": "Stromatoveris",
+    "57268f05dd62a815002e8993": "ctenophores",
+    "57268f05dd62a815002e8994": "Ediacaran",
+    "57269016708984140094ca41": "Bilateria",
+    "57269016708984140094ca42": "Porifera",
+    "572691bedd62a815002e89dc": "beroids",
+    "572691bedd62a815002e89dd": "monophyletic",
+    "572691bedd62a815002e89de": "65.5 million years ago",
+    "572691bedd62a815002e89df": "Richard Harbison",
+    "5725ce4d38643c19005acd4d": "Fresno",
+    "5725ce4d38643c19005acd4e": "220 miles",
+    "5725ce4d38643c19005acd4f": "ash tree",
+    "5725ce4d38643c19005acd50": "an ash leaf",
+    "5725ce4d38643c19005acd51": "ash tree",
+    "5725cfd0271a42140099d225": "1872",
+    "5725cfd0271a42140099d226": "worried about flooding",
+    "5725cfd0271a42140099d227": "1885",
+    "5725cfd0271a42140099d228": "47",
+    "5725cfd0271a42140099d229": "the Central Pacific Railroad",
+    "5725d183271a42140099d23d": "2.7%",
+    "5725d183271a42140099d23e": "Chinatown",
+    "5725d183271a42140099d23f": "Pinedale",
+    "5725d183271a42140099d240": "an interim facility for the relocation of Fresno area Japanese Americans to internment camps",
+    "5725d183271a42140099d241": "assembly center",
+    "5725d34aec44d21400f3d639": "BankAmericard",
+    "5725d34aec44d21400f3d63a": "BankAmericard",
+    "5725d34aec44d21400f3d63b": "revolve a balance",
+    "5725d34aec44d21400f3d63c": "1976",
+    "5725d34aec44d21400f3d63d": "Visa Inc",
+    "5725d42a89a1e219009abf58": "Bill Aken",
+    "5725d42a89a1e219009abf59": "Bob Gallion",
+    "5725d42a89a1e219009abf5a": "Madera",
+    "5725d42a89a1e219009abf5b": "old country-western show at The Fresno Barn",
+    "5725d42a89a1e219009abf5c": "Lupe Mayorga",
+    "5725d662ec44d21400f3d687": "three",
+    "5725d662ec44d21400f3d688": "Roeding Park",
+    "5725d662ec44d21400f3d689": "Kearney Park",
+    "5725d662ec44d21400f3d68a": "Woodward Park",
+    "5725d662ec44d21400f3d68b": "Kearney Park",
+    "5725d7e438643c19005acdf9": "Between the 1880s and World War II",
+    "5725d7e438643c19005acdfa": "Fresno Carnegie Public Library",
+    "5725d7e438643c19005acdfb": "San Joaquin Light & Power Building",
+    "5725d7e438643c19005acdfc": "Hughes Hotel",
+    "5725daa8ec44d21400f3d6b3": "1964",
+    "5725daa8ec44d21400f3d6b4": "Fulton Mall",
+    "5725daa8ec44d21400f3d6b5": "Pierre-Auguste Renoir",
+    "5725daa8ec44d21400f3d6b6": "near their current locations",
+    "5725daa8ec44d21400f3d6b7": "wide sidewalks",
+    "5725db98ec44d21400f3d6c5": "far southeast side",
+    "5725db98ec44d21400f3d6c6": "Kings Canyon Avenue and Clovis Avenue",
+    "5725db98ec44d21400f3d6c7": "1950s through the 1970s",
+    "5725db98ec44d21400f3d6c8": "Sunnyside",
+    "5725db98ec44d21400f3d6c9": "William P. Bell",
+    "5725dd7d89a1e219009abfea": "Tower Theatre",
+    "5725dd7d89a1e219009abfeb": "1939",
+    "5725dd7d89a1e219009abfec": "water tower",
+    "5725dd7d89a1e219009abfed": "Fresno Normal School",
+    "5725dd7d89a1e219009abfee": "one-half mile",
+    "5725e1c4271a42140099d2d7": "late 1970s",
+    "5725e1c4271a42140099d2d8": "second and third run movies",
+    "5725e1c4271a42140099d2d9": "1978",
+    "5725e1c4271a42140099d2da": "Fresno",
+    "5725e1c4271a42140099d2db": "Evita and The Wiz",
+    "5725e45689a1e219009ac048": "live",
+    "5725e45689a1e219009ac049": "a few hundred feet",
+    "5725e45689a1e219009ac04a": "Tower District",
+    "5725e45689a1e219009ac04b": "Tower District",
+    "5725e45689a1e219009ac04c": "Tower District",
+    "5725e748ec44d21400f3d733": "early twentieth century homes",
+    "5725e748ec44d21400f3d734": "Storybook houses",
+    "5725e748ec44d21400f3d735": "contrasts",
+    "5725e748ec44d21400f3d736": "recent decades",
+    "5725ec8289a1e219009ac0ae": "Huntington Boulevard",
+    "5725ec8289a1e219009ac0af": "William Stranahan",
+    "5725ec8289a1e219009ac0b0": "1914",
+    "5725ec8289a1e219009ac0b1": "267",
+    "5725ec8289a1e219009ac0b2": "Fresno Traction Company",
+    "5725edfe38643c19005ace9f": "Southwest Fresno",
+    "5725edfe38643c19005acea0": "southwest",
+    "5725edfe38643c19005acea1": "African-American",
+    "5725edfe38643c19005acea2": "Hmong or Laotian",
+    "5725edfe38643c19005acea3": "Fresno",
+    "5725f00938643c19005aced7": "M. Theo Kearney",
+    "5725f00938643c19005aced8": "palm trees",
+    "5725f00938643c19005aced9": "Fresno Street and Thorne Ave",
+    "5725f00938643c19005aceda": "Brookhaven",
+    "5725f00938643c19005acedb": "Dogg Pound",
+    "5725f190ec44d21400f3d76f": "between the 1960s and 1990s",
+    "5725f190ec44d21400f3d770": "Fresno and B streets",
+    "5725f190ec44d21400f3d771": "Cargill Meat Solutions and Foster Farms",
+    "5725f190ec44d21400f3d772": "Fresno Chandler Executive Airport is also on the West Side",
+    "5725f190ec44d21400f3d773": "very little",
+    "5725f2c838643c19005aceed": "Ralph Woodward",
+    "5725f2c838643c19005aceee": "300",
+    "5725f2c838643c19005aceef": "2,500",
+    "5725f2c838643c19005acef0": "22",
+    "5725f2c838643c19005acef1": "April through October",
+    "5725f46289a1e219009ac0fa": "1946",
+    "5725f46289a1e219009ac0fb": "William Smilie",
+    "5725f46289a1e219009ac0fc": "Sierra Sky Park Airport",
+    "5725f46289a1e219009ac0fd": "automobiles",
+    "5725f46289a1e219009ac0fe": "there are now numerous such communities across the United States and around the world",
+    "5725f5b1271a42140099d371": "hot and dry",
+    "5725f5b1271a42140099d372": "July",
+    "5725f5b1271a42140099d373": "11.5 inches",
+    "5725f5b1271a42140099d374": "northwest",
+    "5725f5b1271a42140099d375": "December, January and February",
+    "5725f6e138643c19005acf19": "115 \u00b0F",
+    "5725f6e138643c19005acf1a": "January 6, 1913",
+    "5725f6e138643c19005acf1b": "1885",
+    "5725f6e138643c19005acf1c": "2.2 inches",
+    "5725f6e138643c19005acf1d": "3.55 inches",
+    "5725f7cd38643c19005acf23": "494,665",
+    "5725f7cd38643c19005acf24": "30.0%",
+    "5725f7cd38643c19005acf25": "8,525",
+    "5725f7cd38643c19005acf26": "30.0%",
+    "5725f7cd38643c19005acf27": "4,404.5",
+    "5725f8f5ec44d21400f3d7b1": "68,511",
+    "5725f8f5ec44d21400f3d7b2": "19.3%",
+    "5725f8f5ec44d21400f3d7b3": "1,388",
+    "5725f8f5ec44d21400f3d7b4": "3.62",
+    "5725f8f5ec44d21400f3d7b5": "3.07",
+    "5725fabc89a1e219009ac128": "427,652",
+    "5725fabc89a1e219009ac129": "149,025",
+    "5725fabc89a1e219009ac12a": "8.4%",
+    "5725fabc89a1e219009ac12b": "11.2%",
+    "5725fabc89a1e219009ac12c": "4,097.9 people per square mile",
+    "5725fb8138643c19005acf3f": "To avoid interference with existing VHF television stations in the San Francisco Bay Area and those planned for Chico, Sacramento, Salinas, and Stockton",
+    "5725fb8138643c19005acf40": "KMJ-TV",
+    "5725fb8138643c19005acf41": "June 1, 1953",
+    "5725fb8138643c19005acf42": "NBC affiliate KSEE",
+    "5725fb8138643c19005acf43": "KGPE",
+    "5725fcbe271a42140099d3ad": "State Route 99",
+    "5725fcbe271a42140099d3ae": "the Sierra Freeway",
+    "5725fcbe271a42140099d3af": "Eisenhower Freeway",
+    "5725fcbe271a42140099d3b0": "west",
+    "5725fe63ec44d21400f3d7dd": "Fresno",
+    "5725fe63ec44d21400f3d7de": "1950s",
+    "5725fe63ec44d21400f3d7df": "99",
+    "5725fe63ec44d21400f3d7e0": "rapidly raising population and traffic in cities along SR 99, as well as the desirability of Federal funding",
+    "5725ff8238643c19005acf49": "Amtrak San Joaquins",
+    "5725ff8238643c19005acf4a": "Downtown Fresno",
+    "5725ff8238643c19005acf4b": "Bakersfield-Stockton mainlines of the Burlington Northern Santa Fe Railway and Union Pacific Railroad",
+    "5725ff8238643c19005acf4c": "San Joaquin Valley Railroad",
+    "5725ff8238643c19005acf4d": "Fresno",
+    "5725d34089a1e219009abf50": "Distributed Adaptive Message Block Switching with the goal to provide a fault-tolerant, efficient routing method for telecommunication messages",
+    "5725d34089a1e219009abf51": "provide a fault-tolerant, efficient routing method for telecommunication messages",
+    "5725d34089a1e219009abf52": "This concept contrasted and contradicted the theretofore established principles of pre-allocation of network bandwidth",
+    "5725d34089a1e219009abf53": "Davies is credited with coining the modern name packet switching",
+    "572632ceec44d21400f3dc2f": "Distributed Adaptive Message Block Switching",
+    "572632ceec44d21400f3dc30": "to provide a fault-tolerant, efficient routing method for telecommunication messages",
+    "572632ceec44d21400f3dc31": "Donald Davies",
+    "5725d52f89a1e219009abf78": "circuit switching",
+    "5725d52f89a1e219009abf79": "fee per unit of connection time",
+    "5725d52f89a1e219009abf7a": "pre-allocates dedicated network bandwidth specifically for each communication session, each having a constant bit rate and latency between nodes",
+    "572634a789a1e219009ac56c": "circuit switching, a method which pre-allocates dedicated network bandwidth specifically for each communication session",
+    "572634a789a1e219009ac56d": "pre-allocates dedicated network bandwidth specifically for each communication session",
+    "572634a789a1e219009ac56e": "pre-allocates dedicated network bandwidth specifically for each communication session",
+    "572634a789a1e219009ac56f": "a fee per unit of connection time, even when no data is transferred, while packet switching may be characterized by a fee per unit of information transmitted",
+    "5726219489a1e219009ac2ce": "with or without intermediate forwarding nodes",
+    "5726219489a1e219009ac2cf": "asynchronously using first-in, first-out buffering",
+    "5726219489a1e219009ac2d0": "according to a multiple access scheme",
+    "5726356938643c19005ad2ff": "with or without intermediate forwarding nodes",
+    "5726356938643c19005ad300": "asynchronously using first-in, first-out buffering",
+    "5726356938643c19005ad301": "according to a multiple access scheme",
+    "5726249538643c19005ad07f": "survivable communications networks",
+    "5726249538643c19005ad080": "survivable communications networks",
+    "5726249538643c19005ad081": "use of a decentralized network with multiple paths between any two points, dividing user messages into message blocks",
+    "5726249538643c19005ad082": "store and forward switching",
+    "57263677ec44d21400f3dc4a": "report RM 3420 in 1964. Report P-2626 described a general architecture for a large-scale, distributed, survivable communications network",
+    "57263677ec44d21400f3dc4c": "store and forward switching",
+    "57263677ec44d21400f3dc49": "distributed adaptive message block switching",
+    "57263677ec44d21400f3dc4b": "use of a decentralized network with multiple paths between any two points",
+    "572629c6271a42140099d6a3": "independently developed the same message routing methodology",
+    "572629c6271a42140099d6a4": "packet switching",
+    "572629c6271a42140099d6a5": "a nationwide network in the UK",
+    "572629c6271a42140099d6a6": "ARPANET",
+    "5726378238643c19005ad313": "Donald Davies",
+    "5726378238643c19005ad314": "packet switching",
+    "5726378238643c19005ad315": "suggested it for use in the ARPANET",
+    "5726385e271a42140099d797": "each packet includes complete addressing information",
+    "5726385e271a42140099d798": "individually",
+    "5726385e271a42140099d799": "Each packet is labeled with a destination address, source address, and port numbers",
+    "5726385e271a42140099d79a": "the original message/data is reassembled in the correct order",
+    "5726398589a1e219009ac58b": "small",
+    "5726398589a1e219009ac58a": "The packets include a connection identifier rather than address information and are negotiated between endpoints so that they are delivered in order and with error checking",
+    "5726398589a1e219009ac589": "address information",
+    "5726398589a1e219009ac588": "Connection-oriented transmission requires a setup phase in each involved node before any packet is transferred to establish the parameters of communication",
+    "57263b1638643c19005ad333": "a handshake between the communicating parties before any user packets are transmitted",
+    "57263b1638643c19005ad335": "Internet Protocol (IP) at the network layer",
+    "57263b1638643c19005ad334": "Frame Relay was used to interconnect LANs across wide area networks",
+    "57263b1638643c19005ad336": "run IP over ATM or a version of MPLS",
+    "57263c78ec44d21400f3dc7b": "1969",
+    "57263c78ec44d21400f3dc7c": "Two fundamental differences involved the division of functions and tasks between the hosts at the edge of the network and the network core",
+    "57263c78ec44d21400f3dc7d": "The User Datagram Protocol (UDP) is an example of a datagram protocol",
+    "57263c78ec44d21400f3dc7e": "X.25 protocol suite uses this network type",
+    "57263cfcec44d21400f3dc8d": "a plug-n-play system",
+    "57263cfcec44d21400f3dc8e": "allowed local area networks to be established ad hoc without the requirement for a centralized router or server",
+    "57263cfcec44d21400f3dc8f": "addresses",
+    "57263cfcec44d21400f3dc90": "plug-n-play",
+    "57263dcd89a1e219009ac5a2": "The CYCLADES packet switching network",
+    "57263dcd89a1e219009ac5a3": "It was the first network to make the hosts responsible for reliable delivery of data",
+    "57263dcd89a1e219009ac5a4": "It was the first network to make the hosts responsible for reliable delivery of data",
+    "57263dcd89a1e219009ac5a5": "ARPANET architecture",
+    "57263ea0271a42140099d7c3": "a suite of network protocols created by Digital Equipment Corporation, originally released in 1975 in order to connect two PDP-11 minicomputers",
+    "57263ea0271a42140099d7c4": "to connect two PDP-11 minicomputers",
+    "57263ea0271a42140099d7c5": "seven",
+    "57263ea0271a42140099d7c6": "open standards with published specifications",
+    "57263fd138643c19005ad37b": "a data network based on this voice-phone network",
+    "57263fd138643c19005ad37c": "Schenectady, Phoenix, Chicago, and Phoenix",
+    "57263fd138643c19005ad37d": "Warner was right.",
+    "57263fd138643c19005ad37e": "He decided that a time-sharing system, based on Kemney's work at Dartmouth",
+    "5726414e271a42140099d7e5": "to explore computer networking between three of Michigan's public universities as a means to help the state's educational and economic development",
+    "5726414e271a42140099d7e6": "connections to the CDC mainframe",
+    "5726414e271a42140099d7e7": "starting in the mid-1980s",
+    "57264228ec44d21400f3dcf5": "It was founded by former ARPA IPTO director Larry Roberts as a means of making ARPANET technology public",
+    "57264228ec44d21400f3dcf6": "Larry Roberts",
+    "57264228ec44d21400f3dcf7": "making ARPANET technology public",
+    "57264228ec44d21400f3dcf8": "ARPANET technology but changed the host interface to X.25 and the terminal interface to X.29",
+    "57264228ec44d21400f3dcf9": "GTE",
+    "5726431aec44d21400f3dd13": "an international data communications network",
+    "5726431aec44d21400f3dd14": "Users typically connected via dial-up connections or dedicated async connections",
+    "5726431aec44d21400f3dd15": "via dial-up connections or dedicated async connections",
+    "5726431aec44d21400f3dd16": "build their own dedicated networks",
+    "5726431aec44d21400f3dd17": "reach locations not on the private network",
+    "572643de5951261400b5195a": "two",
+    "572643de5951261400b5195b": "Bell Northern Research",
+    "572643de5951261400b5195c": "interconnection of national X.25 networks",
+    "5726446a89cfff1900a8404d": "an Australian public X.25 network operated by Telstra.",
+    "5726446a89cfff1900a8404e": "supporting applications such as on-line betting, financial applications",
+    "5726446a89cfff1900a8404f": "via a dial-up terminal to a PAD",
+    "57264586f1498d1400e8dac6": "the public switched data network operated by the Dutch PTT Telecom",
+    "57264586f1498d1400e8dac7": "the network and the connected users via leased lines",
+    "57264586f1498d1400e8dac8": "the network and the connected users via leased lines",
+    "57264586f1498d1400e8dac9": "incorrect",
+    "5726462b708984140094c117": "The Computer Science Network",
+    "5726462b708984140094c118": "to extend networking benefits",
+    "5726462b708984140094c119": "extend networking benefits, for computer science departments at academic and research institutions that could not be directly connected to ARPANET",
+    "5726472bdd62a815002e8042": "a not-for-profit United States computer networking consortium",
+    "5726472bdd62a815002e8043": "Qwest",
+    "5726472bdd62a815002e8044": "Abilene",
+    "5726472bdd62a815002e8045": "Qwest",
+    "5726472bdd62a815002e8046": "Internet2 Network",
+    "572647e2dd62a815002e805c": "The National Science Foundation Network",
+    "572647e2dd62a815002e805d": "promote advanced research and education networking in the United States",
+    "572647e2dd62a815002e805e": "link researchers to the nation's NSF-funded supercomputing centers",
+    "572648d1708984140094c15d": "Very high-speed Backbone Network Service",
+    "572648d1708984140094c15e": "Very high-speed Backbone Network Service",
+    "572648d1708984140094c15f": "MCI Telecommunications",
+    "572648d1708984140094c160": "more than 100",
+    "572648d1708984140094c161": "The vBNS installed one of the first ever production OC-48c (2.5 Gbit/s) IP links",
+    "57264684708984140094c123": "arid plains of Central Asia",
+    "57264684708984140094c124": "Spreading throughout the Mediterranean and Europe, the Black Death is estimated to have killed 30\u201360% of Europe's total population",
+    "57264684708984140094c125": "30\u201360%",
+    "57264684708984140094c126": "17th century",
+    "57264684708984140094c127": "until the 19th century",
+    "572647935951b619008f6ec9": "The plague disease",
+    "572647935951b619008f6eca": "1338\u201339",
+    "572647935951b619008f6ecb": "China",
+    "572647935951b619008f6ecc": "1331",
+    "572647935951b619008f6ecd": "25 million",
+    "57264845f1498d1400e8db0a": "Genoese traders",
+    "57264845f1498d1400e8db0b": "Jani Beg",
+    "57264845f1498d1400e8db0c": "infected corpses",
+    "57264845f1498d1400e8db0d": "Kaffa",
+    "57264845f1498d1400e8db0e": "war, famine, and weather",
+    "572648ed5951b619008f6f03": "east",
+    "572648ed5951b619008f6f04": "Russia",
+    "572648ed5951b619008f6f05": "France, Spain, Portugal and England",
+    "572648ed5951b619008f6f06": "Germany and Scandinavia",
+    "572648ed5951b619008f6f07": "1349",
+    "57264991f1498d1400e8db2e": "serious depopulation and permanent change in both economic and social structures",
+    "57264991f1498d1400e8db2f": "autumn 1347",
+    "57264991f1498d1400e8db30": "through the port's trade with Constantinople",
+    "57264991f1498d1400e8db31": "the north",
+    "57264a74708984140094c18b": "J.I. Pontanus",
+    "57264a74708984140094c18c": "atra mors",
+    "57264a74708984140094c18d": "Gasquet",
+    "57264a74708984140094c18e": "1823",
+    "57264a74708984140094c18f": "Germany",
+    "57264b3edd62a815002e80aa": "the heavens",
+    "57264b3edd62a815002e80ab": "king of France",
+    "57264b3edd62a815002e80ac": "Miasma theory",
+    "57264b3edd62a815002e80ad": "Miasma theory",
+    "57264c42dd62a815002e80c6": "Yersinia pestis",
+    "57264c42dd62a815002e80c7": "Hong Kong",
+    "57264c42dd62a815002e80c8": "Alexandre Yersin",
+    "57264c42dd62a815002e80c9": "Y. pestis",
+    "57264c42dd62a815002e80ca": "rodents",
+    "57264cc6dd62a815002e80e4": "Francis Aidan Gasquet",
+    "57264cc6dd62a815002e80e5": "some form of the ordinary Eastern or bubonic plague",
+    "57264cc6dd62a815002e80e6": "1908",
+    "57264cc6dd62a815002e80e7": "rats and fleas",
+    "57264cc6dd62a815002e80e8": "Justinian plague",
+    "57264d58f1498d1400e8db7a": "30\u201375%",
+    "57264d58f1498d1400e8db7b": "38\u201341 \u00b0C",
+    "57264d58f1498d1400e8db7c": "80",
+    "57264d58f1498d1400e8db7d": "90 to 95 percent",
+    "57264d58f1498d1400e8db7e": "enlarged lymph nodes",
+    "57264e2f708984140094c1e1": "October 2010",
+    "57264e2f708984140094c1e2": "the role of Yersinia pestis in the Black Death",
+    "57264e2f708984140094c1e3": "Polymerase Chain Reaction (PCR) techniques",
+    "57264e2f708984140094c1e4": "mass graves in northern, central and southern Europe",
+    "57264e2f708984140094c1e5": "the role of Yersinia pestis in the Black Death",
+    "57264f18f1498d1400e8dbae": "genetic branches",
+    "57264f18f1498d1400e8dbaf": "Y. p. orientalis and Y. p. medievalis",
+    "57264f18f1498d1400e8dbb0": "the plague may have entered Europe in two waves",
+    "57264f18f1498d1400e8dbb1": "through the port of Marseille around November 1347",
+    "57264f18f1498d1400e8dbb2": "spring of 1349",
+    "57264fe65951b619008f6fa1": "The results of the Haensch study have since been confirmed and amended",
+    "57264fe65951b619008f6fa2": "East Smithfield burial site in England",
+    "57264fe65951b619008f6fa3": "no longer exist",
+    "57264fe65951b619008f6fa4": "October 2011",
+    "5726509bdd62a815002e815a": "J. F. D. Shrewsbury",
+    "5726509bdd62a815002e815b": "the reported rates of mortality in rural areas during the 14th-century pandemic were inconsistent with the modern bubonic plague",
+    "5726509bdd62a815002e815c": "contemporary accounts were exaggerations",
+    "5726509bdd62a815002e815d": "the first major work to challenge the bubonic plague theory",
+    "5726509bdd62a815002e815e": "Samuel K. Cohn, Jr.",
+    "5726516a708984140094c223": "an epidemiological account",
+    "5726516a708984140094c224": "lack of reliable statistics",
+    "5726516a708984140094c225": "over 100%",
+    "5726516a708984140094c226": "the clergy",
+    "5726516a708984140094c227": "1377",
+    "57265285708984140094c25b": "the DNA results may be flawed and might not have been repeated elsewhere, despite extensive samples from other mass graves",
+    "57265285708984140094c25c": "marginal",
+    "57265285708984140094c25d": "too cold in northern Europe for the survival of fleas",
+    "57265285708984140094c25e": "faster",
+    "57265285708984140094c25f": "5 to 15 years",
+    "5726534d708984140094c26d": "a form of anthrax",
+    "5726534d708984140094c26e": "it may have been a combination of anthrax and other pandemics",
+    "5726534d708984140094c26f": "typhus, smallpox and respiratory infections",
+    "5726534d708984140094c270": "blood poisoning",
+    "5726534d708984140094c271": "25",
+    "5726542ff1498d1400e8dc28": "about a third",
+    "5726542ff1498d1400e8dc29": "Half",
+    "5726542ff1498d1400e8dc2a": "at least some pre-planning and Christian burials",
+    "5726542ff1498d1400e8dc2b": "50%",
+    "5726542ff1498d1400e8dc2c": "isolated areas",
+    "572654e2708984140094c2b7": "14th to 17th centuries",
+    "572654e2708984140094c2b8": "the plague was present somewhere in Europe in every year between 1346 and 1671",
+    "572654e2708984140094c2b9": "almost a million",
+    "5726559edd62a815002e81c8": "propose a range of preincident population figures from as high as 7 million to as low as 4 million in 1300",
+    "5726559edd62a815002e81c9": "1350",
+    "5726559edd62a815002e81ca": "10\u201315%",
+    "5726559edd62a815002e81cb": "1665",
+    "57265642f1498d1400e8dc68": "40,000",
+    "57265642f1498d1400e8dc69": "Russia",
+    "57265642f1498d1400e8dc6b": "Italian Plague of 1629\u20131631",
+    "57265642f1498d1400e8dc6c": "1654",
+    "57265642f1498d1400e8dc6a": "22",
+    "57265700dd62a815002e820e": "1.7 million",
+    "57265700dd62a815002e820f": "about half",
+    "57265700dd62a815002e8210": "half",
+    "57265700dd62a815002e8211": "Sweden v. Russia and allies",
+    "57265700dd62a815002e8212": "1720",
+    "5726577f708984140094c301": "between 1500 and 1850",
+    "5726577f708984140094c302": "30 to 50 thousand",
+    "5726577f708984140094c303": "second quarter of the 19th century",
+    "5726577f708984140094c304": "two-thirds",
+    "572657d9dd62a815002e8230": "melt",
+    "572657d9dd62a815002e8231": "metamorphic rock",
+    "572657d9dd62a815002e8232": "a new magma",
+    "572657d9dd62a815002e8233": "igneous, sedimentary, and metamorphic",
+    "572657d9dd62a815002e8234": "heat and pressure",
+    "57265d08708984140094c397": "seafloor spreading",
+    "57265d08708984140094c398": "the crust and rigid uppermost portion of the upper mantle",
+    "57265d08708984140094c399": "asthenosphere",
+    "57265d08708984140094c39a": "the convection of the mantle",
+    "57265d08708984140094c39b": "1960s",
+    "57265f605951b619008f70db": "divergent boundaries",
+    "57265f605951b619008f70dc": "convergent boundaries",
+    "57265f605951b619008f70dd": "Transform boundaries",
+    "57265f605951b619008f70de": "Alfred Wegener",
+    "57265f605951b619008f70df": "the convecting",
+    "57266193dd62a815002e832a": "seismic",
+    "57266193dd62a815002e832b": "a crust and lithosphere on top, the mantle below",
+    "57266193dd62a815002e832d": "410 and 660 kilometers",
+    "57266193dd62a815002e832e": "wave speeds",
+    "57266193dd62a815002e832c": "a crust and lithosphere on top",
+    "572663a9f1498d1400e8ddf2": "the most recent era",
+    "572663a9f1498d1400e8ddf4": "Quaternary",
+    "572663a9f1498d1400e8ddf5": "Holocene",
+    "572663a9f1498d1400e8ddf6": "Quaternary period",
+    "572665ff708984140094c4c3": "cross-cutting relationships",
+    "572665ff708984140094c4c4": "younger",
+    "572665ff708984140094c4c5": "the key bed",
+    "572665ff708984140094c4c6": "younger",
+    "572667e2f1498d1400e8de90": "xenoliths",
+    "572667e2f1498d1400e8de91": "magma or lava flows",
+    "572667e2f1498d1400e8de92": "clasts",
+    "572667e2f1498d1400e8de93": "The principle of inclusions and components",
+    "572667e2f1498d1400e8de94": "gravel",
+    "572669a9dd62a815002e8416": "faunal succession",
+    "572669a9dd62a815002e8417": "William Smith",
+    "572669a9dd62a815002e8418": "complex",
+    "572669a9dd62a815002e8419": "fossils",
+    "572669a9dd62a815002e841a": "Charles Darwin",
+    "57266c015951b619008f7237": "beginning of the 20th century",
+    "57266c015951b619008f7238": "fossils and stratigraphic",
+    "57266c015951b619008f7239": "new absolute ages",
+    "57266c015951b619008f723a": "one another",
+    "57266c015951b619008f723b": "fossil sequences",
+    "57266e72f1498d1400e8df8c": "Thermochemical techniques",
+    "57266e72f1498d1400e8df8d": "closure temperature",
+    "57266e72f1498d1400e8df8e": "isotope ratios",
+    "57266e72f1498d1400e8df8f": "Dating of lava and volcanic ash layers found within a stratigraphic sequence",
+    "5726701add62a815002e84c8": "horizontal",
+    "5726701add62a815002e84c9": "shallow crust",
+    "5726701add62a815002e84ca": "antiforms",
+    "5726701add62a815002e84cb": "synforms",
+    "5726701add62a815002e84cc": "anticlines and synclines",
+    "572671a5f1498d1400e8dfe4": "Extension",
+    "572671a5f1498d1400e8dfe5": "boudins",
+    "572671a5f1498d1400e8dfe6": "Maria Fold and Thrust Belt",
+    "572671a5f1498d1400e8dfe7": "metamorphosed",
+    "572671a5f1498d1400e8dfe8": "Extension causes the rock units as a whole to become longer and thinner",
+    "572673f5708984140094c69b": "Dikes",
+    "572673f5708984140094c69c": "around the lava tube of a volcano",
+    "572673f5708984140094c69d": "topographic gradients",
+    "572673f5708984140094c69e": "Continual motion",
+    "572673f5708984140094c69f": "Deformational",
+    "57267640f1498d1400e8e074": "layered basaltic lava flows",
+    "57267640f1498d1400e8e075": "the Acasta gneiss of the Slave craton",
+    "57267640f1498d1400e8e076": "sedimentary",
+    "57267640f1498d1400e8e077": "Cambrian time",
+    "57267640f1498d1400e8e078": "northwestern Canada",
+    "572677e7708984140094c723": "the study of rocks",
+    "572677e7708984140094c724": "the study of sedimentary layers",
+    "572677e7708984140094c725": "the study of positions of rock units and their deformation",
+    "572677e7708984140094c726": "soils, rivers, landscapes, and glaciers",
+    "57267947f1498d1400e8e0ec": "identifying rocks in the laboratory are through optical microscopy",
+    "57267947f1498d1400e8e0ed": "birefringence, pleochroism, twinning, and interference",
+    "57267947f1498d1400e8e0ee": "geochemical evolution of rock units",
+    "57267947f1498d1400e8e0ef": "the laboratory",
+    "57267947f1498d1400e8e0f0": "optical",
+    "57267b3c5951b619008f7423": "fluid inclusion data and perform high temperature and pressure physical experiments",
+    "57267b3c5951b619008f7424": "fluid inclusion data and perform high temperature and pressure physical experiments",
+    "57267b3c5951b619008f7425": "metamorphic processes",
+    "57267d52708984140094c7d9": "Structural",
+    "57267d52708984140094c7da": "microscopic analysis of oriented thin sections of geologic samples",
+    "57267d52708984140094c7db": "plot",
+    "57267d52708984140094c7dc": "analog and numerical",
+    "57267ebfdd62a815002e872c": "orogenic wedges",
+    "57267ebfdd62a815002e872d": "orogenic wedges",
+    "57267ebfdd62a815002e872e": "sand",
+    "57267ebfdd62a815002e872f": "all angles remain the same",
+    "57267ebfdd62a815002e8730": "Numerical",
+    "57268066708984140094c821": "stratigraphers",
+    "57268066708984140094c822": "geophysical",
+    "57268066708984140094c823": "Geophysical data and well logs",
+    "57268066708984140094c824": "computer programs",
+    "57268066708984140094c825": "water, coal, and hydrocarbon",
+    "57268220f1498d1400e8e218": "to provide better absolute bounds on the timing and rates of deposition",
+    "57268220f1498d1400e8e216": "biostratigraphers",
+    "57268220f1498d1400e8e219": "Magnetic stratigraphers",
+    "57268220f1498d1400e8e217": "Geochronologists",
+    "572683f95951b619008f7525": "Persia",
+    "572683f95951b619008f7526": "Abu al-Rayhan al-Biruni",
+    "572683f95951b619008f7527": "Shen Kuo",
+    "572683f95951b619008f7528": "Ibn Sina",
+    "572683f95951b619008f7529": "deposition of silt",
+    "57268527708984140094c8bf": "James Hutton",
+    "57268527708984140094c8c0": "Theory of the Earth",
+    "57268527708984140094c8c1": "1795",
+    "57268527708984140094c8c2": "the Earth must be much older than had previously been supposed",
+    "572686ac5951b619008f75a9": "William Maclure",
+    "572686ac5951b619008f75aa": "1809",
+    "572686ac5951b619008f75ab": "1809",
+    "572686ac5951b619008f75ad": "Observations on the Geology of the United States",
+    "572686ac5951b619008f75ac": "American Philosophical Society",
+    "57268882f1498d1400e8e306": "Principles of Geology",
+    "57268882f1498d1400e8e307": "uniformitarianism",
+    "57268882f1498d1400e8e308": "uniformitarianism",
+    "57268882f1498d1400e8e309": "catastrophism",
+    "57268882f1498d1400e8e30a": "Charles Darwin",
+    "5726642f5951b619008f7157": "103",
+    "5726642f5951b619008f7158": "8.5",
+    "5726642f5951b619008f7159": "Eurocities network of European cities",
+    "5726642f5951b619008f715a": "Northumberland",
+    "5726642f5951b619008f715b": "Geordie",
+    "5726651ff1498d1400e8de30": "Robert Curthose",
+    "5726651ff1498d1400e8de31": "wool",
+    "5726651ff1498d1400e8de32": "coal",
+    "5726651ff1498d1400e8de33": "16th",
+    "5726651ff1498d1400e8de34": "the Great North Run",
+    "5726660d5951b619008f71b1": "Pons Aelius",
+    "5726660d5951b619008f71b2": "River Tyne",
+    "5726660d5951b619008f71b3": "2,000",
+    "5726660d5951b619008f71b4": "Hadrian's Wall",
+    "5726660d5951b619008f71b5": "Pictish",
+    "572666d9dd62a815002e83b4": "England",
+    "572666d9dd62a815002e83b5": "Elizabeth",
+    "572666d9dd62a815002e83b6": "25-foot (7.6 m)",
+    "572666d9dd62a815002e83b7": "William the Lion",
+    "572666d9dd62a815002e83b8": "three",
+    "57266783f1498d1400e8de86": "coal",
+    "57266783f1498d1400e8de87": "the Hostmen",
+    "57266783f1498d1400e8de88": "a pointless pursuit",
+    "57266783f1498d1400e8de89": "eccentric",
+    "57266783f1498d1400e8de8a": "ruin him",
+    "57266a15f1498d1400e8def6": "families",
+    "57266a15f1498d1400e8def7": "boats",
+    "57266a15f1498d1400e8def8": "7,000",
+    "57266a15f1498d1400e8def9": "one-third",
+    "57266a15f1498d1400e8defa": "devastating loss",
+    "57266ab3dd62a815002e8434": "the King",
+    "57266ab3dd62a815002e8435": "the Scots",
+    "57266ab3dd62a815002e8436": "drummes",
+    "57266ab3dd62a815002e8437": "Triumphing by a brave defence",
+    "57266ab3dd62a815002e8438": "Charles I",
+    "57266cc9f1498d1400e8df53": "urbanization",
+    "57266cc9f1498d1400e8df54": "Maling company",
+    "57266cc9f1498d1400e8df55": "electric lighting",
+    "57266cc9f1498d1400e8df52": "prosperity",
+    "57266cc9f1498d1400e8df56": "steam turbine",
+    "57266dd4f1498d1400e8df78": "medieval",
+    "57266dd4f1498d1400e8df79": "Narrow alleys",
+    "57266dd4f1498d1400e8df7a": "Stairs",
+    "57266dd4f1498d1400e8df7b": "modern buildings",
+    "57266dd4f1498d1400e8df7c": "a restaurant",
+    "57266ec2dd62a815002e84a0": "Tyneside Classical",
+    "57266ec2dd62a815002e84a1": "England's best-looking city",
+    "57266ec2dd62a815002e84a2": "Grey Street",
+    "57266ec2dd62a815002e84a3": "1960s",
+    "57266ec2dd62a815002e84a4": "Shopping Centre",
+    "57266fa1f1498d1400e8dfa4": "the Town Moor",
+    "57266fa1f1498d1400e8dfa5": "graze",
+    "57266fa1f1498d1400e8dfa7": "The Hoppings funfair",
+    "57266fa1f1498d1400e8dfa8": "June",
+    "57266fa1f1498d1400e8dfa6": "Honorary freemen",
+    "57267076708984140094c601": "Large-scale regeneration",
+    "57267076708984140094c602": "Gateshead Council",
+    "57267076708984140094c603": "Norman Foster",
+    "57267076708984140094c604": "to spearhead the regeneration of the North-East",
+    "57267076708984140094c605": "ten",
+    "572671165951b619008f72b7": "Grainger Town area",
+    "572671165951b619008f72b8": "between 1835 and 1842",
+    "572671165951b619008f72b9": "four stories",
+    "572671165951b619008f72ba": "244",
+    "572671e55951b619008f72d7": "Butcher Market",
+    "572671e55951b619008f72d8": "1835",
+    "572671e55951b619008f72d9": "2000",
+    "572671e55951b619008f72da": "a painting",
+    "572671e55951b619008f72db": "English Heritage",
+    "57267383dd62a815002e8552": "oceanic",
+    "57267383dd62a815002e8553": "warming",
+    "57267383dd62a815002e8554": "rain",
+    "57267383dd62a815002e8555": "August 1990",
+    "57267383dd62a815002e8556": "British Isles",
+    "572674f6f1498d1400e8e03a": "2010",
+    "572674f6f1498d1400e8e03b": "Eldon Square Shopping Centre",
+    "572674f6f1498d1400e8e03c": "Bainbridge's",
+    "572674f6f1498d1400e8e03d": "by department",
+    "572674f6f1498d1400e8e03e": "2007",
+    "5726769c708984140094c711": "shopping",
+    "5726769c708984140094c712": "suburban",
+    "5726769c708984140094c713": "Tesco",
+    "5726769c708984140094c714": "the MetroCentre",
+    "5726769c708984140094c715": "Gateshead",
+    "5726778df1498d1400e8e0ac": "The Tyneside flat",
+    "5726778df1498d1400e8e0ad": "terraces",
+    "5726778df1498d1400e8e0ae": "the Ouseburn valley",
+    "5726778df1498d1400e8e0af": "Architects",
+    "5726778df1498d1400e8e0b0": "high density",
+    "572678c0dd62a815002e863e": "7.8%",
+    "572678c0dd62a815002e863f": "5.9%",
+    "572678c0dd62a815002e8640": "overinflated",
+    "572678c0dd62a815002e8641": "authorities",
+    "572678c0dd62a815002e8642": "Tunbridge Wells",
+    "572679c35951b619008f73db": "2001",
+    "572679c35951b619008f73dc": "metropolitan borough of Newcastle",
+    "572679c35951b619008f73dd": "student",
+    "572679c35951b619008f73de": "student",
+    "572679c35951b619008f73df": "student populations",
+    "57267a94f1498d1400e8e122": "37.8",
+    "57267a94f1498d1400e8e123": "Many people in the city have Scottish or Irish ancestors",
+    "57267a94f1498d1400e8e124": "Border Reiver",
+    "57267a94f1498d1400e8e125": "500 and 2,000",
+    "57267a94f1498d1400e8e126": "1%",
+    "57267b65dd62a815002e86a6": "Geordie",
+    "57267b65dd62a815002e86a7": "Anglo-Saxon populations",
+    "57267b65dd62a815002e86a8": "many elements",
+    "57267b65dd62a815002e86a9": "teach",
+    "57267b65dd62a815002e86aa": "stream",
+    "57267c63dd62a815002e86d6": "Scandinavia",
+    "57267c63dd62a815002e86d7": "Northern United Kingdom",
+    "57267c63dd62a815002e86d8": "Scots",
+    "57267c63dd62a815002e86d9": "Canny",
+    "57267c63dd62a815002e86da": "Dutch gooien",
+    "57267ce7708984140094c7c9": "a report",
+    "57267ce7708984140094c7ca": "noisiest",
+    "57267ce7708984140094c7cb": "80.4 decibels",
+    "57267ce7708984140094c7cc": "negative",
+    "57267ce7708984140094c7cd": "a motorway underpass without pedestrian access",
+    "57267de1f1498d1400e8e194": "Collingwood Street",
+    "57267de1f1498d1400e8e195": "a new indoor complex",
+    "57267de1f1498d1400e8e196": "12",
+    "57267de1f1498d1400e8e197": "The Pink Triangle",
+    "57267de1f1498d1400e8e198": "bars, caf\u00e9s and clubs",
+    "57267f695951b619008f74bd": "theatre",
+    "57267f695951b619008f74be": "Stephen Kemble",
+    "57267f695951b619008f74bf": "many celebrated seasons",
+    "57267f695951b619008f74c0": "21 January 1788",
+    "57267f695951b619008f74c1": "Grey Street",
+    "5726800add62a815002e8750": "theatres",
+    "5726800add62a815002e8751": "Theatre Royal",
+    "5726800add62a815002e8752": "Royal Shakespeare Company",
+    "5726800add62a815002e8753": "local talent",
+    "5726800add62a815002e8754": "the arts capital of the UK",
+    "572680865951b619008f74e7": "The Literary and Philosophical Society of Newcastle upon Tyne",
+    "572680865951b619008f74e8": "8000",
+    "572680865951b619008f74e9": "John and Benjamin Green",
+    "572680865951b619008f74ea": "lecture theatre",
+    "572680865951b619008f74eb": "Joseph Swan",
+    "57268109f1498d1400e8e1fa": "Newcastle Beer Festival",
+    "57268109f1498d1400e8e1fb": "May",
+    "57268109f1498d1400e8e1fc": "biennial",
+    "57268109f1498d1400e8e1fd": "EAT! NewcastleGateshead",
+    "57268109f1498d1400e8e1fe": "2",
+    "57268341f1498d1400e8e23c": "The Hoppings",
+    "57268341f1498d1400e8e23d": "June",
+    "57268341f1498d1400e8e23e": "Temperance Movement",
+    "57268341f1498d1400e8e23f": "cycling",
+    "57268341f1498d1400e8e240": "Northern Pride",
+    "572683e6f1498d1400e8e24c": "Newcastle Mela",
+    "572683e6f1498d1400e8e24e": "Norman Foster designed Sage Gateshead Music and Arts Centre",
+    "572683e6f1498d1400e8e24f": "Design Event festival",
+    "572683e6f1498d1400e8e250": "East Asian",
+    "572683e6f1498d1400e8e24d": "NewcastleGateshead",
+    "5726847f708984140094c8ab": "folk-rock",
+    "5726847f708984140094c8ac": "1971",
+    "5726847f708984140094c8ad": "Venom",
+    "5726847f708984140094c8ae": "Skyclad",
+    "5726847f708984140094c8af": "Duran Duran",
+    "57268525dd62a815002e8806": "November 2006 and May 2008",
+    "57268525dd62a815002e8807": "Old Town Hall, Gateshead",
+    "57268525dd62a815002e8808": "three",
+    "57268525dd62a815002e8809": "Classic",
+    "57268525dd62a815002e880a": "roof extension",
+    "57268692dd62a815002e8826": "Newcastle",
+    "57268692dd62a815002e8827": "life on Tyneside",
+    "57268692dd62a815002e8828": "shipbuilding",
+    "57268692dd62a815002e8829": "2009",
+    "57268692dd62a815002e882a": "Seven Stories",
+    "57268731f1498d1400e8e2e2": "the Night of the Fire",
+    "57268731f1498d1400e8e2e3": "Get Carter",
+    "57268731f1498d1400e8e2e4": "noir",
+    "57268731f1498d1400e8e2e5": "Mike Figgis",
+    "57268731f1498d1400e8e2e6": "Mike Figgis and starring Tommy Lee Jones",
+    "572687e1dd62a815002e8852": "Gosforth Park",
+    "572687e1dd62a815002e8853": "Newcastle Eagles",
+    "572687e1dd62a815002e8854": "Newcastle Diamonds",
+    "572687e1dd62a815002e8855": "Brough Park",
+    "572687e1dd62a815002e8856": "Blaydon Race",
+    "57268885dd62a815002e886a": "6 miles",
+    "57268885dd62a815002e886b": "Metro Light Rail system",
+    "57268885dd62a815002e886c": "20 minutes",
+    "57268885dd62a815002e886d": "over five million",
+    "57268885dd62a815002e886e": "over 90",
+    "572689385951b619008f761b": "the Victorian architecture",
+    "572689385951b619008f761c": "six",
+    "572689385951b619008f761d": "Queen Victoria",
+    "572689385951b619008f761e": "Robert Stephenson",
+    "572689385951b619008f761f": "Manors",
+    "572689b6dd62a815002e8890": "three hours",
+    "572689b6dd62a815002e8891": "three",
+    "572689b6dd62a815002e8892": "Edinburgh",
+    "572689b6dd62a815002e8893": "CrossCountry",
+    "572689b6dd62a815002e8894": "Northern Rail",
+    "57268a8fdd62a815002e88ce": "Tyne and Wear Metro",
+    "57268a8fdd62a815002e88cf": "five",
+    "57268a8fdd62a815002e88d0": "deep-level",
+    "57268a8fdd62a815002e88d1": "A bridge",
+    "57268a8fdd62a815002e88d2": "37 million",
+    "57268bb25951b619008f7645": "Metro: All Change",
+    "57268bb25951b619008f7646": "smart ticketing",
+    "57268bb25951b619008f7647": "tracks, signalling and overhead wires",
+    "57268bb25951b619008f7648": "procurement of an entirely new fleet of trains",
+    "57268bb25951b619008f7649": "trams",
+    "57268d1b708984140094c9cd": "A1",
+    "57268d1b708984140094c9ce": "A69",
+    "57268d1b708984140094c9cf": "Gateshead Newcastle Western Bypass",
+    "57268d1b708984140094c9d0": "A1058 \"Coast Road\"",
+    "57268d1b708984140094c9d1": "the capacity of the Tyne Tunnel",
+    "57268ff9f1498d1400e8e3e6": "3",
+    "57268ff9f1498d1400e8e3e7": "two",
+    "57268ff9f1498d1400e8e3e8": "Stagecoach",
+    "57268ff9f1498d1400e8e3e9": "the Tyne and Wear Passenger Transport Executive",
+    "57268ff9f1498d1400e8e3ea": "Go-Ahead",
+    "57269120708984140094ca59": "1998",
+    "57269120708984140094ca5a": "cycling",
+    "57269120708984140094ca5b": "healthy",
+    "57269120708984140094ca5c": "one way streets",
+    "57269120708984140094ca5d": "national networks",
+    "5726934f5951b619008f771d": "Danish DFDS Seaways",
+    "5726934f5951b619008f771e": "October 2006",
+    "5726934f5951b619008f771f": "high fuel prices and new competition from low-cost air services",
+    "5726934f5951b619008f7720": "late 2008",
+    "5726934f5951b619008f7721": "Thomson cruise lines",
+    "57269526dd62a815002e8a4e": "eleven",
+    "57269526dd62a815002e8a4f": "seven",
+    "57269526dd62a815002e8a50": "Royal Grammar School",
+    "57269526dd62a815002e8a51": "Newcastle College",
+    "57269526dd62a815002e8a52": "Catholic",
+    "57269698dd62a815002e8a6c": "two",
+    "57269698dd62a815002e8a6d": "Newcastle University and Northumbria University. Newcastle University",
+    "57269698dd62a815002e8a6e": "Sunday Times University of the Year award",
+    "57269698dd62a815002e8a6f": "polytechnics became new universities",
+    "57269698dd62a815002e8a70": "Northumbria University",
+    "572699b55951b619008f778f": "three",
+    "572699b55951b619008f7790": "1474",
+    "572699b55951b619008f7791": "Coptic Cathedral",
+    "572699b55951b619008f7792": "St Thomas the Martyr",
+    "572699b55951b619008f7793": "parish churches",
+    "57269b165951b619008f77b3": "St Andrew",
+    "57269b165951b619008f77b4": "1726",
+    "57269b165951b619008f77b5": "the main porch",
+    "57269b165951b619008f77b6": "the last of the ancient churchyards to retain its original character",
+    "57269b165951b619008f77b7": "The church tower",
+    "57269c26f1498d1400e8e4ca": "City Road",
+    "57269c26f1498d1400e8e4cb": "Watermark business park",
+    "57269c26f1498d1400e8e4cc": "studio 5",
+    "57269c26f1498d1400e8e4cd": "colouring",
+    "57269c26f1498d1400e8e4ce": "BBC Radio Newcastle",
+    "57269d745951b619008f77d7": "NE1fm",
+    "57269d745951b619008f77d8": "Newcastle Student Radio",
+    "57269d745951b619008f77d9": "since 1951",
+    "57269d745951b619008f77da": "Radio Lollipop",
+    "57269d745951b619008f77db": "Newcastle University's student's union building",
+    "57269e80f1498d1400e8e520": "1770",
+    "57269e80f1498d1400e8e521": "Archbishop of Westminster",
+    "57269e80f1498d1400e8e522": "Lord Armstrong",
+    "57269e80f1498d1400e8e523": "incandescent light bulb",
+    "57269e80f1498d1400e8e524": "Thailand",
+    "57269fab5951b619008f7807": "Rutherford Grammar School",
+    "57269fab5951b619008f7808": "international footballers",
+    "57269fab5951b619008f7809": "Nobel Prize",
+    "57269fab5951b619008f780a": "keyed Northumbrian smallpipes",
+    "57269fab5951b619008f780b": "Newcastle",
+    "5726710b708984140094c61d": "London",
+    "5726710b708984140094c61e": "4.5 million",
+    "5726710b708984140094c61f": "1852",
+    "5726710b708984140094c620": "Queen Victoria and Prince Albert",
+    "572680ac708984140094c83d": "Kensington and Chelsea",
+    "572680ac708984140094c83e": "1852",
+    "572680ac708984140094c83f": "Queen Victoria and Prince Albert",
+    "572680ac708984140094c840": "Department for Culture, Media and Sport",
+    "572680ac708984140094c841": "2001",
+    "57268294708984140094c877": "12.5",
+    "57268294708984140094c878": "145",
+    "57268294708984140094c879": "5,000",
+    "57268294708984140094c87a": "Europe, North America, Asia and North Africa",
+    "57268294708984140094c87b": "ancient times to the present day",
+    "572685cd5951b619008f7573": "Great Exhibition of 1851",
+    "572685cd5951b619008f7574": "Henry Cole",
+    "572685cd5951b619008f7575": "Museum of Manufactures",
+    "572685cd5951b619008f7576": "Somerset House",
+    "572685cd5951b619008f7577": "Gottfried Semper",
+    "57268d02f1498d1400e8e378": "Queen Victoria",
+    "57268d02f1498d1400e8e379": "22 June 1857",
+    "57268d02f1498d1400e8e37a": "George Wallis",
+    "57268d02f1498d1400e8e37b": "late night openings",
+    "57268d02f1498d1400e8e37c": "1949",
+    "57268f2c708984140094ca25": "between September and November 1946",
+    "57268f2c708984140094ca26": "nearly a million and a half",
+    "57268f2c708984140094ca27": "Council of Industrial Design",
+    "57268f2c708984140094ca28": "Festival of Britain",
+    "57268f2c708984140094ca29": "1948",
+    "572691d7708984140094ca6d": "a rock concert",
+    "572691d7708984140094ca6e": "Gryphon",
+    "572691d7708984140094ca6f": "Roy Strong",
+    "572691d7708984140094ca70": "rock",
+    "57269656708984140094cafd": "Dundee",
+    "57269656708984140094cafe": "\u00a376 million",
+    "57269656708984140094caff": "on the city's waterfront",
+    "57269656708984140094cb00": "fashion, architecture, product design, graphic arts and photography",
+    "57269656708984140094cb01": "within five years",
+    "572698d7dd62a815002e8a98": "Brompton Park House",
+    "572698d7dd62a815002e8a99": "Sheepshanks Gallery",
+    "572698d7dd62a815002e8a9a": "Captain Francis Fowke",
+    "572698d7dd62a815002e8a9b": "1862",
+    "572698d7dd62a815002e8a9c": "offices and board room",
+    "57269c06708984140094cba1": "Owen Jones",
+    "57269c06708984140094cba2": "Italian Renaissance",
+    "57269c06708984140094cba3": "James Gamble & Reuben Townroe",
+    "57269c06708984140094cba4": "Isaac Newton",
+    "57269c06708984140094cba5": "Titian",
+    "57269d68708984140094cbd7": "Philip Webb and William Morris",
+    "57269d68708984140094cbd8": "Edward Burne-Jones",
+    "57269d68708984140094cbd9": "James Gamble",
+    "57269d68708984140094cbda": "Alfred Stevens",
+    "57269d68708984140094cbdb": "Sir Edward Poynter",
+    "5726a0205951b619008f781b": "Colonel (later Major General) Henry Young Darracott Scott",
+    "5726a0205951b619008f781c": "School for Naval Architects",
+    "5726a0205951b619008f781d": "Cadeby stone",
+    "5726a0205951b619008f781e": "prints, drawings, paintings and photographs",
+    "5726a0205951b619008f781f": "2008",
+    "5726a2445951b619008f7861": "sgraffito",
+    "5726a2445951b619008f7862": "Starkie Gardner",
+    "5726a2445951b619008f7863": "to the southeast of the garden",
+    "5726a2445951b619008f7864": "south side of the garden",
+    "5726a2445951b619008f7865": "Reuben Townroe",
+    "5726a5b5f1498d1400e8e5fe": "Aston Webb",
+    "5726a5b5f1498d1400e8e5ff": "red brick and Portland stone",
+    "5726a5b5f1498d1400e8e600": "720 feet",
+    "5726a5b5f1498d1400e8e601": "an open work crown surmounted by a statue of fame",
+    "5726a5b5f1498d1400e8e602": "top row of windows",
+    "5726a9ff708984140094cd4b": "Alfred Drury",
+    "5726a9ff708984140094cd4c": "four",
+    "5726a9ff708984140094cd4d": "Webb",
+    "5726a9ff708984140094cd4e": "marble",
+    "5726a9ff708984140094cd4f": "Queen Victoria",
+    "5726ace8dd62a815002e8c9e": "Art Library",
+    "5726ace8dd62a815002e8c9f": "Henry Cole wing",
+    "5726ace8dd62a815002e8ca0": "a new entrance building",
+    "5726ace8dd62a815002e8ca1": "Christopher Hay and Douglas Coyne",
+    "5726ace8dd62a815002e8ca2": "the Spiral",
+    "5726afeb708984140094cdd7": "silverware gallery",
+    "5726afeb708984140094cdd8": "mosaic floors",
+    "5726afeb708984140094cdd9": "FuturePlan",
+    "5726afeb708984140094cdda": "South Kensington",
+    "5726afeb708984140094cddb": "Eva Ji\u0159i\u010dn\u00e1",
+    "5726b12f5951b619008f7aaf": "Kim Wilkie",
+    "5726b12f5951b619008f7ab0": "John Madejski Garden",
+    "5726b12f5951b619008f7ab1": "elliptical",
+    "5726b12f5951b619008f7ab2": "receptions, gatherings or exhibition purposes",
+    "5726b12f5951b619008f7ab3": "American Sweetgum tree",
+    "5726b2fcf1498d1400e8e7e4": "2004",
+    "5726b2fcf1498d1400e8e7e5": "Royal Institute of British Architects",
+    "5726b2fcf1498d1400e8e7e7": "600,000",
+    "5726b2fcf1498d1400e8e7e6": "RIBA Drawings and Archives Collection",
+    "5726b2fcf1498d1400e8e7e8": "700,000",
+    "5726b6e05951b619008f7b99": "Andrea Palladio",
+    "5726b6e05951b619008f7b9a": "Inigo Jones",
+    "5726b6e05951b619008f7b9b": "330",
+    "5726b6e05951b619008f7b9c": "Andrea Palladio",
+    "5726b6e05951b619008f7b9d": "Andrea Palladio",
+    "5726ba83dd62a815002e8e6a": "Bishopsgate",
+    "5726ba83dd62a815002e8e6b": "Great Fire of London",
+    "5726ba83dd62a815002e8e6c": "c1600",
+    "5726ba83dd62a815002e8e6d": "chateau of Montal",
+    "5726ba83dd62a815002e8e6e": "Alhambra",
+    "5726bc505951b619008f7c79": "19,000",
+    "5726bc505951b619008f7c7a": "2006",
+    "5726bc505951b619008f7c7b": "Ardabil Carpet",
+    "5726bc505951b619008f7c7c": "Spain",
+    "5726bc505951b619008f7c7d": "1909",
+    "5726bdc6f1498d1400e8e9c0": "60,000",
+    "5726bdc6f1498d1400e8e9c1": "10,000",
+    "5726bdc6f1498d1400e8e9c2": "6000",
+    "5726bdc6f1498d1400e8e9c4": "1991",
+    "5726bdc6f1498d1400e8e9c3": "Jawaharlal Nehru",
+    "5726bf325951b619008f7cfd": "70,000",
+    "5726bf325951b619008f7cfe": "China, Japan and Korea",
+    "5726bf325951b619008f7cff": "T. T. Tsui Gallery of Chinese art",
+    "5726bf325951b619008f7d00": "1991",
+    "5726bf325951b619008f7d01": "Ming and Qing",
+    "5726c4b5dd62a815002e8fd8": "Ando Cloisonn\u00e9",
+    "5726c4b5dd62a815002e8fd9": "1986",
+    "5726c4b5dd62a815002e8fda": "13th",
+    "5726c4b5dd62a815002e8fdb": "1550 to 1900",
+    "5726c4b5dd62a815002e8fdc": "bronze",
+    "5726c80c5951b619008f7de7": "14th to the 19th century",
+    "5726c80c5951b619008f7de8": "South East Asia",
+    "5726c80c5951b619008f7de9": "Hindu and Buddhist sculptures",
+    "5726c80c5951b619008f7dea": "mother-of-pearl",
+    "5726c80c5951b619008f7deb": "ivory",
+    "5726c9a4708984140094d16f": "Leonardo da Vinci",
+    "5726c9a4708984140094d170": "Forster I, Forster II, and Forster III",
+    "5726c9a4708984140094d171": "14,000",
+    "5726c9a4708984140094d172": "1869",
+    "5726c9a4708984140094d173": "1876",
+    "5726cc11dd62a815002e9086": "William Morris",
+    "5726cc11dd62a815002e9087": "Charles Dickens",
+    "5726cc11dd62a815002e9088": "12th to 16th",
+    "5726cc11dd62a815002e9089": "the trial and rehabilitation of Joan of Arc",
+    "5726cc11dd62a815002e908a": "Lucas Horenbout",
+    "5726ce11f1498d1400e8ebc4": "Word and Image Department",
+    "5726ce11f1498d1400e8ebc5": "MODES cataloging system",
+    "5726ce11f1498d1400e8ebc6": "Encoded Archival Description",
+    "5726ce11f1498d1400e8ebc8": "those were newly accessioned into the collection",
+    "5726ce11f1498d1400e8ebc7": "Search the Collections",
+    "5726cfa3708984140094d209": "2007",
+    "5726cfa3708984140094d20a": "the Factory Project",
+    "5726cfa3708984140094d20b": "Andy Warhol",
+    "5726cfa3708984140094d20c": "15,000",
+    "5726cfa3708984140094d20d": "catalog everything",
+    "5726d4a45951b619008f7f69": "British patrons",
+    "5726d4a45951b619008f7f6a": "Asia",
+    "5726d4a45951b619008f7f6b": "Gian Lorenzo Bernini",
+    "5726d4a45951b619008f7f6c": "Horace Walpole",
+    "5726d4a45951b619008f7f6d": "porcelain, cloth and wallpaper",
+    "5726d7faf1498d1400e8ecdb": "tea drinking",
+    "5726d7faf1498d1400e8ecda": "design in the Tudor period was influenced by the spread of printed books",
+    "5726d7faf1498d1400e8ecde": "John Ruskin",
+    "5726d7faf1498d1400e8ecdc": "mass production",
+    "5726d7faf1498d1400e8ecdd": "Arts and Crafts",
+    "5726d993f1498d1400e8ed16": "Trajan's Column",
+    "5726d993f1498d1400e8ed17": "cut in half",
+    "5726d993f1498d1400e8ed18": "David",
+    "5726d993f1498d1400e8ed19": "sculptures, friezes and tombs",
+    "5726d993f1498d1400e8ed1a": "in a glass case",
+    "5726dba1dd62a815002e92e4": "1731",
+    "5726dba1dd62a815002e92e5": "Frederick II the Great",
+    "5726dba1dd62a815002e92e6": "1762",
+    "5726dba1dd62a815002e92e7": "1909",
+    "5726dba1dd62a815002e92e8": "the museum's stock of Chinese and Japanese ceramics",
+    "5726dd71f1498d1400e8edd8": "Josiah Wedgwood, William De Morgan and Bernard Leach",
+    "5726dd71f1498d1400e8edd9": "Britain and Holland",
+    "5726dd71f1498d1400e8edda": "ceramic stoves",
+    "5726dd71f1498d1400e8eddb": "16th and 17th centuries",
+    "5726dd71f1498d1400e8eddc": "Germany and Switzerland",
+    "5726de7a5951b619008f80ab": "4000",
+    "5726de7a5951b619008f80ac": "over 6000",
+    "5726de7a5951b619008f80ad": "Ancient Egypt",
+    "5726de7a5951b619008f80af": "Ren\u00e9 Lalique",
+    "5726de7a5951b619008f80ae": "Ren\u00e9 Lalique",
+    "5726e06df1498d1400e8ee52": "1994",
+    "5726e06df1498d1400e8ee53": "Danny Lane",
+    "5726e06df1498d1400e8ee54": "2004",
+    "5726e06df1498d1400e8ee55": "Dale Chihuly",
+    "5726e06df1498d1400e8ee56": "13th",
+    "5726e1fcdd62a815002e93c4": "over 10,000",
+    "5726e1fcdd62a815002e93c5": "2,000",
+    "5726e1fcdd62a815002e93c6": "Lord Frederic Leighton, Sir Samuel Luke Fildes and Aubrey Beardsley. Modern British artists represented in the collection include: Paul Nash",
+    "5726e1fcdd62a815002e93c7": "Paul Nash",
+    "5726e1fcdd62a815002e93c8": "Jean Auguste Dominique Ingres",
+    "5726e37ef1498d1400e8eed8": "14,000",
+    "5726e37ef1498d1400e8eed9": "Word and Image",
+    "5726e37ef1498d1400e8eeda": "Because everyday clothing from previous eras has not generally survived",
+    "5726e37ef1498d1400e8eedb": "1913",
+    "5726e37ef1498d1400e8eedc": "Harrods",
+    "5726e4eedd62a815002e9438": "2002",
+    "5726e4eedd62a815002e9439": "Vivienne Westwood",
+    "5726e4eedd62a815002e943a": "178",
+    "5726e4eedd62a815002e943b": "178 Vivienne Westwood",
+    "5726e4eedd62a815002e943c": "modern fashion",
+    "5726e680dd62a815002e946e": "Italian and French Renaissance",
+    "5726e680dd62a815002e946f": "between 1859 and 1865",
+    "5726e680dd62a815002e9470": "French 18th-century art and furnishings",
+    "5726e680dd62a815002e9471": "1882",
+    "5726e680dd62a815002e9472": "\u00a3250,000",
+    "5726e834dd62a815002e94a4": "1580",
+    "5726e834dd62a815002e94a5": "Hans Vredeman de Vries",
+    "5726e834dd62a815002e94a6": "c1750",
+    "5726e834dd62a815002e94a7": "Germany",
+    "5726e834dd62a815002e94a8": "Ernest Gimson, Edward William Godwin, Charles Voysey, Adolf Loos and Otto Wagner",
+    "5726e9c65951b619008f8247": "over 6000",
+    "5726e9c65951b619008f8248": "Ancient Egypt",
+    "5726e9c65951b619008f8249": "1869",
+    "5726e9c65951b619008f824a": "154",
+    "5726e9c65951b619008f824b": "William and Judith Bollinger",
+    "5726ee28708984140094d656": "secular and sacred",
+    "5726ee28708984140094d657": "1496\u201397",
+    "5726ee28708984140094d658": "8",
+    "5726ee28708984140094d659": "Sir George Gilbert Scott",
+    "5726ee28708984140094d655": "over 10,000",
+    "5726ef12dd62a815002e959e": "c1110",
+    "5726ef12dd62a815002e959f": "gilt bronze",
+    "5726ef12dd62a815002e95a0": "St Thomas Becket",
+    "5726ef12dd62a815002e95a1": "c1180",
+    "5726ef12dd62a815002e95a2": "gilt copper",
+    "5726f1ec708984140094d6aa": "5,100",
+    "5726f1ec708984140094d6ad": "Bryan Davies",
+    "5726f1ec708984140094d6ab": "the Horniman Museum",
+    "5726f1ec708984140094d6ac": "35",
+    "5726f1ec708984140094d6a9": "2010",
+    "5726f2bcf1498d1400e8f0bc": "1130",
+    "5726f2bcf1498d1400e8f0bd": "650",
+    "5726f2bcf1498d1400e8f0be": "6800",
+    "5726f2bcf1498d1400e8f0bf": "Her Majesty the Queen Elizabeth II",
+    "5726f2bcf1498d1400e8f0c0": "Andr\u00e9s Marzal De Sax",
+    "5726f4a0708984140094d6e9": "1857",
+    "5726f4a0708984140094d6ea": "233",
+    "5726f4a0708984140094d6eb": "forming a 'A National Gallery of British Art",
+    "5726f4a0708984140094d6ec": "The Hay Wain",
+    "5726f4a0708984140094d6ed": "British",
+    "5726f755708984140094d737": "continental art",
+    "5726f755708984140094d738": "Madame de Pompadour",
+    "5726f755708984140094d739": "Carlo Crivelli's Virgin and Child",
+    "5726f755708984140094d73a": "Fran\u00e7ois, Duc d'Alen\u00e7on",
+    "5726f90b708984140094d75d": "Eadweard Muybridge",
+    "5726f90b708984140094d75e": "1887",
+    "5726f90b708984140094d75f": "781",
+    "5726f90b708984140094d760": "images of different animals and humans performimg various actions",
+    "5726f90b708984140094d761": "James Lafayette",
+    "5726fb18dd62a815002e96ce": "400 AD to 1914",
+    "5726fb18dd62a815002e96cf": "22,000",
+    "5726fb18dd62a815002e96d0": "400 AD to 1914",
+    "5726fb18dd62a815002e96d1": "from tomb and memorial",
+    "5726fc63dd62a815002e9706": "National Galleries of Scotland",
+    "5726fc63dd62a815002e9707": "Neptune and Triton",
+    "5726fc63dd62a815002e9708": "the Chancel Chapel from Santa Chiara Florence",
+    "5726fc63dd62a815002e9709": "Giuliano da Sangallo",
+    "5726fc63dd62a815002e970a": "1493\u20131500",
+    "5726fe95dd62a815002e9732": "20",
+    "5726fe95dd62a815002e9733": "the sculptor",
+    "5726fe95dd62a815002e9734": "1914",
+    "5726fe95dd62a815002e9735": "World War I",
+    "5726fe95dd62a815002e9736": "statue of St John the Baptist",
+    "572700c8dd62a815002e976a": "Nicholas Stone",
+    "572700c8dd62a815002e976b": "Nicholas Stone",
+    "572700c8dd62a815002e976c": "Nicholas Stone",
+    "572700c8dd62a815002e976d": "Sculptors",
+    "572702a3dd62a815002e9790": "Dorothy and Michael Hintze",
+    "572702a3dd62a815002e9791": "1950",
+    "572702a3dd62a815002e9792": "by theme, tomb sculpture, portraiture, garden sculpture and mythology",
+    "572702a3dd62a815002e9793": "Henry Moore and Jacob Epstein",
+    "572702a3dd62a815002e9794": "Tate Britain",
+    "572703fdf1498d1400e8f23c": "53,000",
+    "572703fdf1498d1400e8f23d": "all populated continents",
+    "572703fdf1498d1400e8f23e": "1st century AD",
+    "572703fdf1498d1400e8f23f": "western European",
+    "572703fdf1498d1400e8f240": "technique",
+    "57270541dd62a815002e97c8": "the Cloth of St Gereon",
+    "57270541dd62a815002e97c9": "15th",
+    "57270541dd62a815002e97ca": "Netherlands",
+    "57270541dd62a815002e97cb": "various animals",
+    "57270541dd62a815002e97cc": "John Vanderbank",
+    "57270676dd62a815002e97f0": "late 14th-century",
+    "57270676dd62a815002e97f1": "William Morris",
+    "57270676dd62a815002e97f2": "1887",
+    "57270676dd62a815002e97f3": "Marion Dorn",
+    "57270676dd62a815002e97f4": "Serge Chermayeff",
+    "57270817708984140094d8c7": "Theatre Museum",
+    "57270817708984140094d8c8": "2009",
+    "57270817708984140094d8c9": "the UK's biggest national collection of material about live performance in the UK since Shakespeare's day",
+    "57270817708984140094d8ca": "Shakespeare's day",
+    "57270817708984140094d8cb": "research, exhibitions and other shows",
+    "57270ab9708984140094d8f7": "Conservation",
+    "57270ab9708984140094d8f8": "temperature and light",
+    "57270ab9708984140094d8f9": "Interventive treatment",
+    "57270ab9708984140094d8fa": "Museum of Childhood",
+    "57270ab9708984140094d8fb": "preventive",
+    "57267b755951b619008f7433": "Disney\u2013ABC Television Group",
+    "57267b755951b619008f7434": "1957",
+    "57267b755951b619008f7435": "Manhattan",
+    "57267b755951b619008f7436": "Columbus Avenue and West 66th Street",
+    "57267b755951b619008f7437": "Disney Media Networks",
+    "57267ca75951b619008f7469": "October 12, 1943",
+    "57267ca75951b619008f746a": "radio",
+    "57267ca75951b619008f746b": "1948",
+    "57267ca75951b619008f746c": "ESPN",
+    "57267ca75951b619008f746d": "Capital Cities Communications",
+    "57267f1cdd62a815002e8740": "232",
+    "57267f1cdd62a815002e8741": "Citadel Broadcasting",
+    "57267f1cdd62a815002e8742": "eight",
+    "57267f1cdd62a815002e8743": "Canadian Radio-television and Telecommunications Commission",
+    "57267f1cdd62a815002e8744": "Citadel Broadcasting",
+    "5726808bdd62a815002e8776": "Radio Corporation of America",
+    "5726808bdd62a815002e8777": "NBC Blue and NBC Red",
+    "5726808bdd62a815002e8778": "lesser importance",
+    "5726808bdd62a815002e8779": "drama series",
+    "5726808bdd62a815002e877a": "NBC Red",
+    "572681ab708984140094c85d": "Mutual",
+    "572681ab708984140094c85e": "1938",
+    "572681ab708984140094c85f": "1940",
+    "572681ab708984140094c860": "NBC Red Network",
+    "572681ab708984140094c861": "NBC Blue",
+    "572684f5dd62a815002e87fc": "Mark Woods",
+    "572684f5dd62a815002e87fd": "NBC Blue Network",
+    "572684f5dd62a815002e87fe": "Dillon, Read & Co.",
+    "572684f5dd62a815002e87ff": "David Sarnoff",
+    "572684f5dd62a815002e8800": "$7.5 million",
+    "572685d1f1498d1400e8e29e": "Life Savers candy",
+    "572685d1f1498d1400e8e29f": "October 12, 1943",
+    "572685d1f1498d1400e8e2a0": "George B. Storer",
+    "572685d1f1498d1400e8e2a1": "president and CEO",
+    "572685d1f1498d1400e8e2a2": "June 30, 1951",
+    "57268739708984140094c8ed": "Magnetophon tape recorder",
+    "57268739708984140094c8ee": "Paul Whiteman",
+    "57268739708984140094c8ef": "ABC",
+    "57268739708984140094c8f0": "Bing Crosby",
+    "57268739708984140094c8f1": "public service",
+    "57269260dd62a815002e89ea": "$155 million",
+    "57269260dd62a815002e89eb": "ABC1",
+    "57269260dd62a815002e89ec": "September 8, 2007",
+    "57269260dd62a815002e89ed": "ABC International",
+    "57269260dd62a815002e89ee": "United States",
+    "57269344f1498d1400e8e43e": "1959",
+    "57269344f1498d1400e8e43f": "satellite television",
+    "57269344f1498d1400e8e440": "Japan and Latin America",
+    "57269344f1498d1400e8e441": "The arrival of satellite television",
+    "5726caaaf1498d1400e8eb5c": "coronation of Queen Elizabeth II",
+    "5726caaaf1498d1400e8eb5d": "Beirut",
+    "5726caaaf1498d1400e8eb5e": "Mainichi Broadcasting System",
+    "5726caaaf1498d1400e8eb5f": "technical problems and flight delays",
+    "5726caaaf1498d1400e8eb60": "technical problems and flight delays",
+    "5726e5ac708984140094d51b": "Peanuts",
+    "5726e5ac708984140094d51c": "Emmy Awards",
+    "5726e5ac708984140094d51d": "1965",
+    "5726e5ac708984140094d51e": "Academy Awards",
+    "5726e5ac708984140094d51f": "Charlie Brown Christmas",
+    "5726e5b1f1498d1400e8ef30": "1974",
+    "5726e5b1f1498d1400e8ef31": "Ryan Seacrest",
+    "5726e5b1f1498d1400e8ef32": "1954",
+    "5726e5b1f1498d1400e8ef33": "ABC",
+    "5726e5b1f1498d1400e8ef34": "TLC",
+    "5726e671dd62a815002e9464": "General Hospital",
+    "5726e671dd62a815002e9465": "1975",
+    "5726e671dd62a815002e9466": "General Hospital",
+    "5726e671dd62a815002e9467": "The View and The Chew",
+    "5726e671dd62a815002e9468": "1963",
+    "5726e773f1498d1400e8ef6e": "X Games",
+    "5726e773f1498d1400e8ef6c": "2006",
+    "5726e773f1498d1400e8ef6b": "12:00 to 6:00 p.m. Eastern Time",
+    "5726e773f1498d1400e8ef6a": "NBA",
+    "5726e773f1498d1400e8ef6d": "Open Championship golf and The Wimbledon tennis tournaments",
+    "5726e860708984140094d579": "Frank Marx",
+    "5726e860708984140094d57a": "2 through 6",
+    "5726e860708984140094d57b": "1947",
+    "5726e860708984140094d57c": "VHF channel 7",
+    "5726e942f1498d1400e8efa0": "108",
+    "5726e942f1498d1400e8efa1": "two",
+    "5726e942f1498d1400e8efa2": "DuMont Television Network",
+    "5726e942f1498d1400e8efa3": "CBS and NBC",
+    "5726ea06dd62a815002e950a": "U.S. Supreme Court",
+    "5726ea06dd62a815002e950b": "Paramount Pictures",
+    "5726ea06dd62a815002e950c": "nine",
+    "5726ea06dd62a815002e950d": "CBS",
+    "5726ea06dd62a815002e950e": "Prudential Insurance Company of America",
+    "5726ec6ff1498d1400e8eff2": "Leonard Goldenson",
+    "5726ec6ff1498d1400e8eff3": "William S. Paley",
+    "5726ec6ff1498d1400e8eff4": "June 6, 1951",
+    "5726ed12708984140094d645": "1952",
+    "5726ed12708984140094d646": "February 9, 1953",
+    "5726ed12708984140094d647": "American Broadcasting-Paramount Theatres, Inc.",
+    "5726ed12708984140094d648": "1501 Broadway in Manhattan",
+    "5726edeff1498d1400e8f024": "August 10, 1948",
+    "5726edeff1498d1400e8f025": "October 1948",
+    "5726edeff1498d1400e8f026": "Mount Wilson",
+    "5726edeff1498d1400e8f027": "Prospect Studios",
+    "5726efdbdd62a815002e95c6": "September 30, 1960",
+    "5726efdbdd62a815002e95c7": "1960s",
+    "5726efdbdd62a815002e95c8": "William Hanna and Joseph Barbera",
+    "5726efdbdd62a815002e95c9": "1960s",
+    "5726f0865951b619008f82e5": "1959",
+    "5726f0865951b619008f82e6": "NBC",
+    "5726f0865951b619008f82e7": "1961",
+    "5726f0865951b619008f82e8": "1985",
+    "572734af708984140094dae3": "its circle logo",
+    "572734af708984140094dae4": "Troika Design Group",
+    "572734af708984140094dae5": "black-and-yellow",
+    "572734af708984140094dae6": "the dot",
+    "572735a15951b619008f86bf": "Pittard Sullivan",
+    "572735a15951b619008f86c0": "2015",
+    "572735a15951b619008f86c1": "We Love TV",
+    "572735a15951b619008f86c2": "on Demand",
+    "572736625951b619008f86d1": "1993\u201394",
+    "572736625951b619008f86d2": "1995\u201396 season",
+    "572736625951b619008f86d3": "1983",
+    "572736625951b619008f86d4": "That Special Feeling",
+    "572736fc5951b619008f86d9": "1977",
+    "572736fc5951b619008f86da": "black",
+    "572736fc5951b619008f86db": "black",
+    "57273799f1498d1400e8f4be": "Paul Rand",
+    "57273799f1498d1400e8f4bf": "Bauhaus",
+    "57273799f1498d1400e8f4c0": "Herbert Bayer",
+    "57273799f1498d1400e8f4c1": "1963\u201364 season",
+    "5727387b5951b619008f86e9": "ABC Radio",
+    "5727387b5951b619008f86ea": "October 19, 2005",
+    "5727387b5951b619008f86eb": "six",
+    "57273954708984140094db05": "2004",
+    "57273954708984140094db06": "Dancing with the Stars",
+    "57273954708984140094db07": "Anne Sweeney",
+    "57273954708984140094db08": "NASCAR",
+    "57273a0d708984140094db0d": "2002",
+    "57273a0d708984140094db0e": "Michael Eisner",
+    "57273a0d708984140094db0f": "The Bachelor",
+    "57273a0d708984140094db10": "The Bachelorette",
+    "57273abef1498d1400e8f4da": "Time Warner Cable",
+    "57273abef1498d1400e8f4db": "TWC",
+    "57273abef1498d1400e8f4dc": "NBC",
+    "57273abef1498d1400e8f4dd": "May 2",
+    "57273b69dd62a815002e99d6": "2000",
+    "57273b69dd62a815002e99d7": "WB",
+    "57273b69dd62a815002e99d8": "CBS",
+    "57273c195951b619008f8721": "August 1999",
+    "57273c195951b619008f8722": "Regis Philbin",
+    "57273c195951b619008f8723": "Buena Vista Television",
+    "57273c195951b619008f8724": "Meredith Vieira",
+    "57273d19708984140094db3d": "July 31, 1995",
+    "57273d19708984140094db3e": "ABC Inc",
+    "57273d19708984140094db3f": "Knight Ridder",
+    "57273d19708984140094db40": "Robert Iger",
+    "57273d19708984140094db41": "Sports Night",
+    "57273dbbf1498d1400e8f508": "1965\u201366",
+    "57273dbbf1498d1400e8f509": "third",
+    "57273dbbf1498d1400e8f50a": "Beating the Odds: The Untold Story Behind the Rise of ABC",
+    "57273ef15951b619008f8751": "May 1, 1953",
+    "57273ef15951b619008f8752": "7 West 66th Street",
+    "57273ef15951b619008f8753": "Baltimore",
+    "57273f9d708984140094db51": "Robert Kintner",
+    "57273f9d708984140094db52": "DuMont Television Network",
+    "57273f9d708984140094db53": "ABC-DuMont",
+    "57273f9d708984140094db54": "$5 million",
+    "5727403af1498d1400e8f526": "Paramount Pictures",
+    "5727403af1498d1400e8f527": "The Lone Ranger",
+    "5727403af1498d1400e8f528": "The Adventures of Ozzie and Harriet",
+    "57274118dd62a815002e9a1c": "Cheyenne",
+    "57274118dd62a815002e9a1d": "Sugarfoot",
+    "57274118dd62a815002e9a1e": "Maverick",
+    "57274118dd62a815002e9a1f": "Warner Bros. Presents",
+    "572741aaf1498d1400e8f53e": "Roy",
+    "572741aaf1498d1400e8f53f": "$500,000",
+    "572741aaf1498d1400e8f540": "1954",
+    "572741aaf1498d1400e8f541": "Disneyland",
+    "572742daf1498d1400e8f550": "Allen Shaw",
+    "572742daf1498d1400e8f551": "Harold L. Neal",
+    "572742daf1498d1400e8f552": "LOVE Radio",
+    "572742daf1498d1400e8f553": "seven",
+    "5727436af1498d1400e8f558": "1969",
+    "5727436af1498d1400e8f559": "Duel",
+    "5727436af1498d1400e8f55a": "1971",
+    "5727436af1498d1400e8f55b": "$400,000\u2013$450,000",
+    "572743fb708984140094db93": "1970s",
+    "572743fb708984140094db94": "CBS and NBC",
+    "572743fb708984140094db95": "behavioral and demographic",
+    "5727448b5951b619008f87a1": "Monday Night Football",
+    "5727448b5951b619008f87a2": "2006",
+    "5727448b5951b619008f87a3": "NBC",
+    "5727448b5951b619008f87a4": "15%\u201316%",
+    "57274633dd62a815002e9a4e": "1970",
+    "57274633dd62a815002e9a4f": "1972",
+    "57274633dd62a815002e9a50": "Worldvision Enterprises",
+    "572746d3dd62a815002e9a66": "cigarette advertising",
+    "572746d3dd62a815002e9a67": "January 2, 1971",
+    "572746d3dd62a815002e9a68": "Henry Plitt",
+    "572746d3dd62a815002e9a69": "Elton Rule",
+    "572747dd5951b619008f87a9": "1966",
+    "572747dd5951b619008f87aa": "Happy Days",
+    "572747dd5951b619008f87ab": "developing youth-oriented programming",
+    "572747dd5951b619008f87ac": "Paramount Pictures",
+    "572748745951b619008f87b1": "Fred Pierce",
+    "572748745951b619008f87b2": "Fred Silverman",
+    "572748745951b619008f87b3": "S.W.A.T",
+    "572748745951b619008f87b4": "November 3, 1975",
+    "5727492f708984140094dbb5": "president of NBC's entertainment division",
+    "5727492f708984140094dbb6": "Laverne & Shirley",
+    "5727492f708984140094dbb7": "jiggle TV",
+    "572749d7dd62a815002e9a90": "Alex Haley",
+    "572749d7dd62a815002e9a91": "Aaron Spelling",
+    "572749d7dd62a815002e9a92": "nine",
+    "572749d7dd62a815002e9a93": "1976\u201377",
+    "572749d7dd62a815002e9a94": "Soap",
+    "57274a8cf1498d1400e8f5b4": "Roone Arledge",
+    "57274a8cf1498d1400e8f5b5": "ABC Sports",
+    "57274a8cf1498d1400e8f5b6": "7 Lincoln Square",
+    "57274a8cf1498d1400e8f5b7": "June 1979",
+    "57274baff1498d1400e8f5dc": "June 1978",
+    "57274baff1498d1400e8f5dd": "Hugh Downs",
+    "57274baff1498d1400e8f5de": "Barbara Walters",
+    "57274baff1498d1400e8f5df": "MCA Inc.",
+    "57274cac708984140094dbdd": "ABC Cable News",
+    "57274cac708984140094dbde": "ABC News Now",
+    "57274cac708984140094dbdf": "WJRT-TV",
+    "57274cac708984140094dbe0": "WJRT-TV and WTVG",
+    "57274d905951b619008f87e1": "Writers Guild of America",
+    "57274d905951b619008f87e2": "Duel",
+    "57274d905951b619008f87e3": "Caris & Co.",
+    "57274e6a5951b619008f87f1": "ABC Entertainment",
+    "57274e6a5951b619008f87f2": "ABC Entertainment Group",
+    "57274e6a5951b619008f87f3": "Citadel Media",
+    "57274e6a5951b619008f87f4": "iTunes",
+    "57274f07708984140094dbed": "2004",
+    "57274f07708984140094dbee": "2004",
+    "57274f07708984140094dbef": "Fridays",
+    "57274f07708984140094dbf0": "Fridays",
+    "5727504b5951b619008f881d": "1970",
+    "5727504b5951b619008f881e": "Worldvision Enterprises",
+    "5727504b5951b619008f881f": "ABC Circle Films",
+    "5727504b5951b619008f8820": "Turner Broadcasting System",
+    "57275273dd62a815002e9b16": "Disney",
+    "57275273dd62a815002e9b17": "Buena Vista Television",
+    "57275273dd62a815002e9b18": "Buena Vista International Television",
+    "57275273dd62a815002e9b19": "Selznick library",
+    "57275339dd62a815002e9b28": "WABC-TV and WPVI-TV",
+    "57275339dd62a815002e9b29": "eight",
+    "57275339dd62a815002e9b2a": "235",
+    "57275339dd62a815002e9b2b": "96.26%",
+    "572754b5dd62a815002e9b44": "1946",
+    "572754b5dd62a815002e9b45": "the seal of the Federal Communications Commission",
+    "572754b5dd62a815002e9b46": "1943",
+    "57275573708984140094dc45": "2011",
+    "57275573708984140094dc46": "Extreme Makeover: Home Edition",
+    "57275573708984140094dc47": "HD",
+    "57275573708984140094dc48": "Litton's Weekend Aventure",
+    "57275650708984140094dc5f": "720p high definition",
+    "57275650708984140094dc60": "1080i HD",
+    "57275650708984140094dc61": "11",
+    "57275650708984140094dc62": "720p high definition",
+    "57275743f1498d1400e8f680": "Body of Proof",
+    "57275743f1498d1400e8f681": "Happy Endings",
+    "57275743f1498d1400e8f682": "NBC",
+    "57275743f1498d1400e8f683": "Body of Proof",
+    "5727590df1498d1400e8f6b6": "All My Children and One Life to Live",
+    "5727590df1498d1400e8f6b7": "Prospect Park",
+    "5727590df1498d1400e8f6b8": "Hulu",
+    "5727590df1498d1400e8f6b9": "The Revolution",
+    "5727590df1498d1400e8f6ba": "18\u201349",
+    "572759c1f1498d1400e8f6ca": "2004",
+    "572759c1f1498d1400e8f6cb": "CBS",
+    "572759c1f1498d1400e8f6cc": "The Goldbergs",
+    "572759c1f1498d1400e8f6cd": "The Neighbors",
+    "57275a505951b619008f889f": "The Middle and Modern Family",
+    "57275a505951b619008f88a0": "Dragon's Den",
+    "57275a505951b619008f88a1": "Sundays",
+    "57275a505951b619008f88a2": "Tim Allen",
+    "57275bfb708984140094dc97": "Daniel Burke",
+    "57275bfb708984140094dc98": "Thomas Murphy",
+    "57275bfb708984140094dc99": "NYPD Blue",
+    "57275bfb708984140094dc9a": "Steven Bochco",
+    "57275bfb708984140094dc9b": "ten",
+    "57275cb3f1498d1400e8f6da": "1993",
+    "57275cb3f1498d1400e8f6db": "DIC Entertainment",
+    "57275cb3f1498d1400e8f6dc": "Time Warner Cable",
+    "57275cb3f1498d1400e8f6dd": "23.63%",
+    "57275e125951b619008f88d7": "WLS",
+    "57275e125951b619008f88d8": "May 9, 1960",
+    "57275e125951b619008f88d9": "John Bassett",
+    "57275e125951b619008f88da": "CFTO-TV",
+    "57275e95f1498d1400e8f6f4": "Wide World of Sports",
+    "57275e95f1498d1400e8f6f5": "Edgar Scherick",
+    "57275e95f1498d1400e8f6f6": "Roone Arledge",
+    "57275e95f1498d1400e8f6f7": "Sports Programs, Inc.",
+    "57275f6ef1498d1400e8f706": "American Broadcasting Companies",
+    "57275f6ef1498d1400e8f707": "The Dating Game",
+    "57275f6ef1498d1400e8f708": "The Newlywed Game",
+    "57275f6ef1498d1400e8f709": "1330 Avenue of the Americas in Manhattan",
+    "57276166dd62a815002e9bd8": "90%",
+    "57276166dd62a815002e9bd9": "Dynasty",
+    "57276166dd62a815002e9bda": "Mork & Mindy",
+    "57276166dd62a815002e9bdb": "Alpha Repertory Television Service",
+    "5727623a5951b619008f8921": "Infinity Broadcasting Corporation",
+    "5727623a5951b619008f8922": "Getty Oil",
+    "5727623a5951b619008f8923": "The Entertainment Channel",
+    "5727623a5951b619008f8924": "Arts & Entertainment Television",
+    "572763a8708984140094dcd9": "Daniel B. Burke",
+    "572763a8708984140094dcda": "chairman and CEO",
+    "572763a8708984140094dcdb": "$465 million",
+    "572763a8708984140094dcdc": "America's Funniest Home Videos",
+    "572763a8708984140094dcdd": "Home Improvement",
+    "572764855951b619008f8951": "General Hospital",
+    "572764855951b619008f8952": "The View and The Chew and the soap opera General Hospital",
+    "572764855951b619008f8953": "7:00 to 9:00 a.m",
+    "572764855951b619008f8954": "Jimmy Kimmel Live!",
+    "57276576dd62a815002e9c18": "New Jersey, Rhode Island and Delaware",
+    "57276576dd62a815002e9c19": "WBMA-LD",
+    "57276576dd62a815002e9c1a": "WBND-LD",
+    "57276576dd62a815002e9c1b": "WLQP-LP",
+    "57276690708984140094dd01": "ABC Circle Films",
+    "57276690708984140094dd02": "ABC Studios",
+    "57276690708984140094dd03": "ABC Television Center",
+    "57276690708984140094dd04": "ABC Television Center, East",
+    "5727678e5951b619008f8973": "Times Square Studios",
+    "5727678e5951b619008f8974": "Good Morning America and Nightline",
+    "5727678e5951b619008f8975": "Peter Jennings Way in 2006 in honor of the recently deceased longtime ABC News chief anchor and anchor of World News Tonight",
+    "5727678e5951b619008f8976": "World News Tonight",
+    "572768d9708984140094dd13": "ABC on Demand",
+    "572768d9708984140094dd14": "Hulu",
+    "572768d9708984140094dd15": "July 6, 2009",
+    "572768d9708984140094dd16": "27%",
+    "572769e85951b619008f8985": "the day after their original broadcast",
+    "572769e85951b619008f8986": "eight",
+    "572769e85951b619008f8987": "fast forwarding of accessed content",
+    "572769e85951b619008f8988": "January 7, 2014",
+    "57276a8f5951b619008f8995": "LoyalKaspar",
+    "57276a8f5951b619008f8996": "four",
+    "57276a8f5951b619008f8997": "ABC Modern",
+    "57276a8f5951b619008f8998": "ESPN",
+    "57276c64f1498d1400e8f7b2": "14",
+    "57276c64f1498d1400e8f7b3": "14",
+    "57276c64f1498d1400e8f7b4": "All-Channel Receiver Act",
+    "57276c64f1498d1400e8f7b5": "UHF",
+    "57276d7f708984140094dd3f": "Youngstown",
+    "57276d7f708984140094dd40": "five times",
+    "57276d7f708984140094dd41": "WTRF-TV",
+    "57276d7f708984140094dd42": "1980s",
+    "57276f82dd62a815002e9cd0": "Walt Disney Presents",
+    "57276f82dd62a815002e9cd1": "Desilu Productions",
+    "57276f82dd62a815002e9cd2": "use of violence",
+    "57276f82dd62a815002e9cd3": "April 1959",
+    "5727705f5951b619008f89f3": "ABC Sunday Night Movie",
+    "5727705f5951b619008f89f4": "$15.5 million",
+    "5727705f5951b619008f89f5": "Hanna-Barbera",
+    "5727705f5951b619008f89f6": "The Jetsons",
+    "5727705f5951b619008f89f7": "April 1, 1963",
+    "572771a5f1498d1400e8f840": "ITT",
+    "572771a5f1498d1400e8f841": "Donald F. Turner",
+    "572771a5f1498d1400e8f842": "Department of Justice",
+    "572771a5f1498d1400e8f843": "January 1, 1968",
+    "57277373dd62a815002e9d24": "Capital Cities Communications",
+    "57277373dd62a815002e9d25": "$3.5 billion",
+    "57277373dd62a815002e9d26": "Warren Buffett",
+    "57277373dd62a815002e9d27": "E. W. Scripps Company",
+    "57277373dd62a815002e9d28": "12",
+    "572774cf5951b619008f8a51": "September 5, 1985",
+    "572774cf5951b619008f8a52": "Capital Cities/ABC, Inc",
+    "572774cf5951b619008f8a53": "president of ABC's broadcasting division",
+    "572774cf5951b619008f8a54": "Michael P. Millardi",
+    "572774cf5951b619008f8a55": "Roone Arledge",
+    "57277585708984140094de2b": "Laverne & Shirley",
+    "57277585708984140094de2c": "Three's Company",
+    "57277585708984140094de2d": "NBC",
+    "57277585708984140094de2e": "The Love Boat",
+    "57277585708984140094de2f": "comedies",
+    "57277632f1498d1400e8f8c4": "TGIF",
+    "57277632f1498d1400e8f8c5": "Thank Goodness It's Funny",
+    "57277632f1498d1400e8f8c6": "Miller-Boyett Productions",
+    "57277632f1498d1400e8f8c7": "Warner Bros",
+    "572776e85951b619008f8a7f": "seven",
+    "572776e85951b619008f8a80": "Charly",
+    "572776e85951b619008f8a81": "Ralph Nelson",
+    "572776e85951b619008f8a82": "1985",
+    "572776e85951b619008f8a83": "Redwood City, California",
+    "5727780a5951b619008f8a9d": "westerns and detective series",
+    "5727780a5951b619008f8a9e": "500%",
+    "5727780a5951b619008f8a9f": "between 10% and 18%",
+    "5727780a5951b619008f8aa0": "Ollie Treiz",
+    "5727780a5951b619008f8aa1": "Dick Clark",
+    "57277944f1498d1400e8f90a": "counterprogramming against its competitors",
+    "57277944f1498d1400e8f90b": "Zorro",
+    "57277944f1498d1400e8f90c": "Life",
+    "57277944f1498d1400e8f90d": "detective shows",
+    "57277af2708984140094dec3": "WATCH ABC",
+    "57277af2708984140094dec4": "New York City O&O WABC-TV and Philadelphia O&O WPVI-TV",
+    "57277af2708984140094dec5": "Hearst Television",
+    "57277af2708984140094dec6": "WatchESPN",
+    "57277bfc708984140094ded9": "Sinclair",
+    "57277bfc708984140094deda": "WABM-DT2/WDBB-DT2",
+    "57277bfc708984140094dedb": "E. W. Scripps Company",
+    "57277bfc708984140094dedc": "28",
+    "57277bfc708984140094dedd": "15",
+    "57277cf6dd62a815002e9e76": "Start Here",
+    "57277cf6dd62a815002e9e77": "Troika",
+    "57277cf6dd62a815002e9e78": "entertainment",
+    "57277cf6dd62a815002e9e79": "ABC News",
+    "57277de9dd62a815002e9ea4": "WFTS-TV and WWSB",
+    "57277de9dd62a815002e9ea5": "KMBC-TV and KQTV",
+    "57277de9dd62a815002e9ea6": "WZZM and WOTV",
+    "57277de9dd62a815002e9ea7": "WTSP",
+    "57269e8a5951b619008f77f5": "Mongol Empire",
+    "57269e8a5951b619008f77f6": "nomadic tribes of Northeast Asia",
+    "57269e8a5951b619008f77f7": "Khwarezmian and Xia controlled lands",
+    "57269e8a5951b619008f77f8": "Central Asia and China",
+    "57269e8a5951b619008f77f9": "Western Xia and Jin dynasties",
+    "5726a00d708984140094cc25": "\u00d6gedei Khan",
+    "5726a00d708984140094cc26": "1227",
+    "5726a00d708984140094cc27": "Mongol",
+    "5726a00d708984140094cc28": "sons and grandsons",
+    "5726a00d708984140094cc29": "Mongolia",
+    "5726a21bf1498d1400e8e578": "Del\u00fc\u00fcn Boldog",
+    "5726a21bf1498d1400e8e579": "Yes\u00fcgei",
+    "5726a21bf1498d1400e8e57a": "1162",
+    "5726a21bf1498d1400e8e57b": "Tem\u00fcjin-\u00fcge",
+    "5726a340dd62a815002e8bbc": "Tem\u00fclen",
+    "5726a340dd62a815002e8bbd": "Hasar, Hachiun, and Tem\u00fcge",
+    "5726a340dd62a815002e8bbe": "B\u00f6rte",
+    "5726a340dd62a815002e8bbf": "Khongirad",
+    "5726a340dd62a815002e8bc0": "Dai Setsen",
+    "5726a4a9708984140094ccb7": "Begter",
+    "5726a4a9708984140094ccb8": "Temujin's mother Hoelun",
+    "5726a4a9708984140094ccb9": "Khasar",
+    "5726a4a9708984140094ccba": "one hunting excursion",
+    "5726a5f65951b619008f7903": "Tayichi'ud",
+    "5726a5f65951b619008f7904": "with a cangue, a sort of portable stocks",
+    "5726a5f65951b619008f7905": "Jelme and Bo'orchu",
+    "5726a5f65951b619008f7906": "Jelme and Bo'orchu",
+    "5726a5f65951b619008f7907": "a river crevice",
+    "5726a784708984140094ccff": "arranged marriages were often used to solidify temporary alliances",
+    "5726a784708984140094cd00": "Hoelun",
+    "5726a784708984140094cd01": "Chinese dynasties to the south",
+    "5726a784708984140094cd02": "need for alliances",
+    "5726a8d4dd62a815002e8c34": "Onggirat",
+    "5726a8d4dd62a815002e8c35": "Merkits",
+    "5726a8d4dd62a815002e8c36": "Jamukha",
+    "5726a8d4dd62a815002e8c37": "Jochi",
+    "5726a8d4dd62a815002e8c38": "1185\u20131226",
+    "5726a9ed5951b619008f7969": "three",
+    "5726a9ed5951b619008f796a": "Chagatai (1187\u20141241), \u00d6gedei (1189\u20141241), and Tolui",
+    "5726a9ed5951b619008f796b": "1241",
+    "5726a9ed5951b619008f796c": "Chagatai (1187\u20141241), \u00d6gedei (1189\u20141241), and Tolui",
+    "5726a9ed5951b619008f796d": "at least six",
+    "5726ab47f1498d1400e8e6a2": "sworn brother or blood brother",
+    "5726ab47f1498d1400e8e6a3": "Toghrul",
+    "5726ab47f1498d1400e8e6a4": "Keraites",
+    "5726ab47f1498d1400e8e6a5": "20,000",
+    "5726ab47f1498d1400e8e6a6": "Jamukha",
+    "5726acc1f1498d1400e8e6ca": "Mongolian aristocracy",
+    "5726acc1f1498d1400e8e6cb": "Kokochu",
+    "5726acc1f1498d1400e8e6cc": "1186",
+    "5726acc1f1498d1400e8e6cd": "Battle of Dalan Balzhut",
+    "5726acc1f1498d1400e8e6ce": "Qara Khitai",
+    "5726ae32708984140094cdab": "the Yassa code",
+    "5726ae32708984140094cdac": "wealth from future possible war spoils",
+    "5726ae32708984140094cdad": "orphans",
+    "5726ae32708984140094cdae": "wealth",
+    "5726af765951b619008f7a51": "Jochi",
+    "5726af765951b619008f7a52": "Jamukha",
+    "5726af765951b619008f7a53": "Jamukha",
+    "5726af765951b619008f7a54": "Keraite",
+    "5726b0985951b619008f7a91": "Naimans",
+    "5726b0985951b619008f7a92": "1201",
+    "5726b0985951b619008f7a93": "universal ruler",
+    "5726b0985951b619008f7a94": "Subutai",
+    "5726b0985951b619008f7a95": "1206",
+    "5726b1d95951b619008f7acd": "friendship and reunion",
+    "5726b1d95951b619008f7ace": "he did not want disloyal men in his army",
+    "5726b1d95951b619008f7acf": "a noble death",
+    "5726b1d95951b619008f7ad0": "noble",
+    "5726b879708984140094cf01": "the Chinese",
+    "5726b879708984140094cf02": "Jamukha",
+    "5726b879708984140094cf03": "Khasar",
+    "5726b879708984140094cf04": "military strategies showed a deep interest in gathering good intelligence and understanding the motivations of his rivals, exemplified by his extensive spy network",
+    "5726b879708984140094cf05": "Wang Khan",
+    "5726b9e15951b619008f7bf1": "1206",
+    "5726b9e15951b619008f7bf2": "Khuruldai",
+    "5726b9e15951b619008f7bf3": "the founder of the Yuan dynasty",
+    "5726b9e15951b619008f7bf4": "\u00d6gedei",
+    "5726b9e15951b619008f7bf5": "Khuruldai",
+    "5726bb645951b619008f7c3b": "Jin",
+    "5726bb645951b619008f7c3c": "Ming-Tan",
+    "5726bb645951b619008f7c3d": "1215",
+    "5726bb645951b619008f7c3e": "Kaifeng",
+    "5726bb645951b619008f7c3f": "\u00d6gedei Khan",
+    "5726bd655951b619008f7c9f": "Genghis Khan",
+    "5726bd655951b619008f7ca0": "the Liao dynasty",
+    "5726bd655951b619008f7ca1": "20,000",
+    "5726bd655951b619008f7ca2": "Jebe",
+    "5726bd655951b619008f7ca3": "The Arrow",
+    "5726bf135951b619008f7ce9": "internal revolt",
+    "5726bf135951b619008f7cea": "west of Kashgar",
+    "5726bf135951b619008f7ceb": "Lake Balkhash",
+    "5726bf135951b619008f7cec": "Khwarezmia",
+    "5726bf135951b619008f7ced": "a Muslim state",
+    "5726c0c5dd62a815002e8f76": "Shah Ala ad-Din Muhammad",
+    "5726c0c5dd62a815002e8f77": "Inalchuq",
+    "5726c0c5dd62a815002e8f78": "two Mongols and a Muslim",
+    "5726c0c5dd62a815002e8f79": "100,000",
+    "5726c0c5dd62a815002e8f7a": "Silk Road",
+    "5726c20fdd62a815002e8fa6": "Tien Shan mountains",
+    "5726c20fdd62a815002e8fa7": "three",
+    "5726c20fdd62a815002e8fa8": "southeast part of Khwarzemia",
+    "5726c20fdd62a815002e8fa9": "Tolui",
+    "5726c20fdd62a815002e8faa": "Samarkand",
+    "5726c3b3708984140094d0cf": "superior strategy and tactics",
+    "5726c3b3708984140094d0d0": "Otrar",
+    "5726c3b3708984140094d0d1": "silver",
+    "5726c3b3708984140094d0d2": "fled",
+    "5726c3b3708984140094d0d3": "Subutai and Jebe",
+    "5726c4c8708984140094d0f5": "Samarkand",
+    "5726c4c8708984140094d0f6": "Bukhara",
+    "5726c4c8708984140094d0f7": "a river through the Khwarezmid emperor's birthplace",
+    "5726c61ff1498d1400e8eae2": "captured enemies",
+    "5726c61ff1498d1400e8eae3": "reneged on his surrender terms and executed every soldier that had taken arms against him",
+    "5726c61ff1498d1400e8eae4": "pyramids of severed heads",
+    "5726c7a85951b619008f7ddd": "opened the gates to the Mongols",
+    "5726c7a85951b619008f7dde": "Turkish defenders",
+    "5726c7a85951b619008f7ddf": "artisans and craftsmen",
+    "5726c7a85951b619008f7de0": "the flail of God",
+    "5726c7a85951b619008f7de1": "young men",
+    "5726c9b0dd62a815002e9046": "1220",
+    "5726c9b0dd62a815002e9047": "Subutai",
+    "5726c9b0dd62a815002e9048": "Russia",
+    "5726c9b0dd62a815002e9049": "Battle of Kalka River",
+    "5726c9b0dd62a815002e904a": "Mstislav the Bold of Halych and Mstislav III of Kiev",
+    "57272bcb5951b619008f8683": "Genghis Khan's grandson Batu and the Golden Horde",
+    "57272bcb5951b619008f8684": "Golden Horde",
+    "57272bcb5951b619008f8685": "Genghis Khan's grandson Batu and the Golden Horde",
+    "57272bcb5951b619008f8686": "1225",
+    "57272bcb5951b619008f8687": "on the road back to Samarkand",
+    "57272ce1f1498d1400e8f43a": "1226",
+    "57272ce1f1498d1400e8f43b": "autumn",
+    "57272ce1f1498d1400e8f43c": "Tangut",
+    "57272ce1f1498d1400e8f43d": "Yellow River",
+    "57272ce1f1498d1400e8f43e": "a line of five stars",
+    "57272dd55951b619008f8697": "Ning Hia",
+    "57272dd55951b619008f8698": "Ma Jianlong",
+    "57272dd55951b619008f8699": "arrows",
+    "57272dd55951b619008f869a": "Liupanshan",
+    "57272dd55951b619008f869b": "executed",
+    "57272ec7f1498d1400e8f44e": "Jochi",
+    "57272ec7f1498d1400e8f44f": "\u00d6gedei",
+    "57272ec7f1498d1400e8f450": "invasion of the Khwarezmid Empire",
+    "57272ec7f1498d1400e8f451": "\u00d6gedei",
+    "57272ff2708984140094dabd": "Chagatai and Jochi",
+    "57272ff2708984140094dabe": "Chagatai",
+    "57272ff2708984140094dabf": "Tolui",
+    "57272ff2708984140094dac0": "\u00d6gedei",
+    "5727311d5951b619008f86ab": "1226",
+    "5727311d5951b619008f86ac": "Khorasan",
+    "5727311d5951b619008f86ad": "Urgench",
+    "5727311d5951b619008f86ae": "Sultan Muhammad",
+    "5727311d5951b619008f86af": "the accuracy of this story is questionable",
+    "57273249f1498d1400e8f46a": "Yinchuan",
+    "57273249f1498d1400e8f46b": "hunting or battle. According to The Secret History of the Mongols Genghis Khan fell from his horse while hunting",
+    "57273249f1498d1400e8f46c": "arrow",
+    "57273249f1498d1400e8f46d": "Western Xia",
+    "57273249f1498d1400e8f46e": "Oirads",
+    "572732f8f1498d1400e8f474": "without markings",
+    "572732f8f1498d1400e8f475": "Khentii Aimag",
+    "572732f8f1498d1400e8f476": "Onon River",
+    "572732f8f1498d1400e8f477": "Genghis Khan Mausoleum",
+    "57273455f1498d1400e8f48c": "Edsen Khoroo",
+    "57273455f1498d1400e8f48d": "Dongshan Dafo Dian",
+    "57273455f1498d1400e8f48e": "Lord's Enclosure",
+    "57273455f1498d1400e8f48f": "1954",
+    "57273455f1498d1400e8f490": "Red Guards",
+    "57273581708984140094daeb": "October 6, 2004",
+    "57273581708984140094daec": "river",
+    "57273581708984140094daed": "Gilgamesh of Uruk and Atilla the Hun",
+    "57273581708984140094daee": "horses",
+    "572736bff1498d1400e8f4b5": "Genghis Khan",
+    "572736bff1498d1400e8f4b4": "the Yassa",
+    "572736bff1498d1400e8f4b6": "meritocracy",
+    "572736bff1498d1400e8f4b7": "Genghis Khan and his family",
+    "572736bff1498d1400e8f4b8": "Muhammad Khan",
+    "57273887dd62a815002e99a2": "tax exemptions",
+    "57273887dd62a815002e99a3": "Ong Khan",
+    "57273887dd62a815002e99a4": "religious tolerance",
+    "57273887dd62a815002e99a5": "Shamanist, Buddhist or Christian",
+    "572739a75951b619008f86f7": "T\u00f6regene Khatun",
+    "572739a75951b619008f86f8": "Pax Mongolica",
+    "572739a75951b619008f86f9": "the Chinese",
+    "572739a75951b619008f86fa": "a civil state",
+    "57273b1a5951b619008f8709": "Chu'Tsai",
+    "57273b1a5951b619008f870a": "nomads",
+    "57273b1a5951b619008f870b": "Jin",
+    "57273b1a5951b619008f870c": "Khitan rulers",
+    "57273c01f1498d1400e8f4e2": "Muqali, Jebe and Subutai",
+    "57273c01f1498d1400e8f4e3": "Karakorum",
+    "57273c01f1498d1400e8f4e4": "Muqali",
+    "57273c01f1498d1400e8f4e5": "Subutai and Jebe",
+    "57273c01f1498d1400e8f4e6": "unwavering loyalty",
+    "57273d06f1498d1400e8f4f6": "certain rivers",
+    "57273d06f1498d1400e8f4f7": "people they conquered",
+    "57273d06f1498d1400e8f4f8": "feigned retreat to break enemy formations and to lure small enemy groups away from the larger group and defended position for ambush and counterattack",
+    "57273d06f1498d1400e8f4f9": "driving them in front of the army",
+    "57273dccdd62a815002e99fa": "Caspian Sea",
+    "57273dccdd62a815002e99fb": "Caspian Sea",
+    "57273dccdd62a815002e99fc": "\u00d6gedei Khan",
+    "57273dccdd62a815002e99fd": "1279",
+    "57273eecf1498d1400e8f51e": "Silk Road",
+    "57273eecf1498d1400e8f51f": "Turkey",
+    "57273eecf1498d1400e8f520": "tolerant",
+    "57273eecf1498d1400e8f521": "increased",
+    "5727404b708984140094db59": "1990s",
+    "5727404b708984140094db5a": "uniting warring tribes",
+    "5727404b708984140094db5b": "Genghis Khan's children",
+    "5727404b708984140094db5c": "his butchery is exaggerated",
+    "5727404b708984140094db5d": "unfairly biased",
+    "57274126dd62a815002e9a24": "t\u00f6gr\u00f6g",
+    "57274126dd62a815002e9a25": "Genghis Khan",
+    "57274126dd62a815002e9a26": "Chinggis Khaan International Airport",
+    "57274126dd62a815002e9a27": "to avoid trivialization",
+    "57274126dd62a815002e9a28": "Ulaanbaatar",
+    "5727515af1498d1400e8f63a": "Ikh Zasag",
+    "5727515af1498d1400e8f63b": "corruption and bribery",
+    "5727515af1498d1400e8f63c": "Elbegdorj",
+    "5727515af1498d1400e8f63d": "traditional Mongolian script",
+    "57275250708984140094dc25": "Inner Mongolia region",
+    "57275250708984140094dc26": "twice",
+    "57275250708984140094dc27": "Kublai Khan",
+    "57275250708984140094dc28": "Yuan",
+    "57275250708984140094dc29": "his grandson",
+    "572753af708984140094dc2f": "Iran",
+    "572753af708984140094dc30": "10 to 15 million",
+    "572753af708984140094dc31": "10 to 15 million",
+    "572754cd5951b619008f8863": "Hulagu Khan",
+    "572754cd5951b619008f8864": "Mamluks of Egypt",
+    "572754cd5951b619008f8865": "Ghazan Khan",
+    "572754cd5951b619008f8866": "1237",
+    "572754cd5951b619008f8867": "Novgorod and Pskov",
+    "572756715951b619008f8877": "Mughal emperors",
+    "572756715951b619008f8878": "Genghis Khan",
+    "572756715951b619008f8879": "Attar of Nishapur",
+    "5727580bf1498d1400e8f69a": "tenggis",
+    "5727580bf1498d1400e8f69b": "Lake Baikal",
+    "5727580bf1498d1400e8f69c": "\"right\", \"just\", or \"true\"",
+    "5727580bf1498d1400e8f69d": "Zh\u00e8ng",
+    "5727580bf1498d1400e8f69e": "Chinggis",
+    "572758c3dd62a815002e9b78": "Chinggis Khaan",
+    "572758c3dd62a815002e9b79": "Cengiz Han, \u00c7ingiz Xan",
+    "572758c3dd62a815002e9b7a": "Ti\u011bm\u00f9zh\u0113n",
+    "572758c3dd62a815002e9b7b": "Chinghiz, Chinghis, and Chingiz",
+    "572758c3dd62a815002e9b7c": "Ch\u00e9ngj\u00eds\u012b H\u00e1n",
+    "5726d8bd708984140094d35b": "pharma",
+    "5726d8bd708984140094d35c": "tobacco and patent medicines",
+    "5726d8bd708984140094d35d": "sorcery or even poison",
+    "5726d8bd708984140094d35e": "outdated or only approproriate",
+    "5726d8bd708984140094d35f": "many other herbs not listed",
+    "5726d9935951b619008f7fed": "healthcare",
+    "5726d9935951b619008f7fee": "optimal",
+    "5726d9935951b619008f7fef": "drug",
+    "5726d9935951b619008f7ff0": "optimisation of a drug treatment for an individual",
+    "5726d9935951b619008f7ff1": "medicines",
+    "5726da89dd62a815002e92b2": "senior pharmacy technicians",
+    "5726da89dd62a815002e92b3": "General Pharmaceutical Council",
+    "5726da89dd62a815002e92b4": "manage the pharmacy department and specialised areas in pharmacy practice",
+    "5726da89dd62a815002e92b5": "health care professional",
+    "5726da89dd62a815002e92b6": "manage the pharmacy department and specialised areas in pharmacy practice",
+    "5726db5add62a815002e92d5": "writing a five volume book",
+    "5726db5add62a815002e92d6": "De Materia Medica",
+    "5726db5add62a815002e92d7": "materia medica",
+    "5726db5add62a815002e92d4": "Diocles of Carystus",
+    "5726db5add62a815002e92d8": "middle eastern scientists",
+    "5726dcbddd62a815002e9320": "highly respected",
+    "5726dcbddd62a815002e9321": "Taih\u014d Code",
+    "5726dcbddd62a815002e9322": "Meiji Restoration",
+    "5726dcbddd62a815002e9323": "Ranked positions",
+    "5726dcbddd62a815002e9324": "In the Imperial household",
+    "5726ddf6f1498d1400e8ee04": "botany and chemistry",
+    "5726ddf6f1498d1400e8ee05": "Muhammad ibn Zakar\u012bya R\u0101zi",
+    "5726ddf6f1498d1400e8ee06": "Abu al-Qasim al-Zahrawi",
+    "5726ddf6f1498d1400e8ee08": "Abu al-Qasim al-Zahrawi",
+    "5726ddf6f1498d1400e8ee07": "sodium carbonate and potassium carbonate",
+    "5726deed5951b619008f80c5": "15th century",
+    "5726deed5951b619008f80c6": "Church of Santa Maria Novella in Florence, Italy",
+    "5726deed5951b619008f80c7": "a museum",
+    "5726deed5951b619008f80c8": "old prescription books and antique drugs",
+    "5726deed5951b619008f80c9": "1221",
+    "5726e08e5951b619008f810f": "pharmacy legislation",
+    "5726e08e5951b619008f8110": "automation to assist them in their new role dealing with patients' prescriptions and patient safety issues",
+    "5726e08e5951b619008f8111": "automation",
+    "5726e08e5951b619008f8112": "patients' prescriptions and patient safety issues",
+    "5726e08e5951b619008f8113": "requirements for storage conditions, compulsory texts, equipment, etc.",
+    "5726e179dd62a815002e93ae": "hospitals",
+    "5726e179dd62a815002e93af": "various disciplines of pharmacy",
+    "5726e179dd62a815002e93b0": "patient compliance issues",
+    "5726e179dd62a815002e93b1": "clinical pharmacists",
+    "5726e313f1498d1400e8eeb2": "within the premises of the hospital",
+    "5726e313f1498d1400e8eeb3": "unit-dose, or a single dose of medicine",
+    "5726e313f1498d1400e8eeb4": "high risk preparations",
+    "5726e313f1498d1400e8eeb5": "high cost of medications and drug-related technology",
+    "5726e313f1498d1400e8eeb6": "more specialized medications",
+    "5726e3c4dd62a815002e9404": "direct patient care services",
+    "5726e3c4dd62a815002e9405": "hospitals and clinics",
+    "5726e3c4dd62a815002e9406": "physicians and other healthcare professionals",
+    "5726e3c4dd62a815002e9407": "patient care rounds drug product selection",
+    "5726e3c4dd62a815002e9408": "all health care settings",
+    "5726e5c95951b619008f81cb": "creating a comprehensive drug therapy plan for patient-specific problems",
+    "5726e5c95951b619008f81cc": "an evaluation of the appropriateness of the drug therapy",
+    "5726e5c95951b619008f81cd": "drug choice, dose, route, frequency, and duration of therapy",
+    "5726e5c95951b619008f81ce": "potential drug interactions, adverse drug reactions, and assess patient drug allergies",
+    "5726e985dd62a815002e94da": "full independent prescribing authority",
+    "5726e985dd62a815002e94db": "North Carolina and New Mexico",
+    "5726e985dd62a815002e94dc": "2011",
+    "5726e985dd62a815002e94dd": "Board Certified Ambulatory Care Pharmacist and these pharmacists will carry the initials BCACP",
+    "5726e985dd62a815002e94de": "VA, the Indian Health Service, and NIH",
+    "5726ea985951b619008f8261": "medication regimen review",
+    "5726ea985951b619008f8262": "nursing homes",
+    "5726ea985951b619008f8263": "Omnicare, Kindred Healthcare and PharMerica",
+    "5726ea985951b619008f8264": "many elderly people are now taking numerous medications but continue to live outside of institutional settings",
+    "5726ea985951b619008f8265": "employ consultant pharmacists and/or provide consulting services",
+    "5726eb4b5951b619008f826b": "about the year 2000",
+    "5726eb4b5951b619008f826c": "brick-and-mortar community pharmacies",
+    "5726eb4b5951b619008f826d": "online pharmacies",
+    "5726eb4b5951b619008f826e": "the method by which the medications are requested and received",
+    "5726eb4b5951b619008f826f": "the method by which the medications are requested and received",
+    "5726ed6cf1498d1400e8f00c": "to avoid the \"inconvenience\" of visiting a doctor or to obtain medications which their doctors were unwilling to prescribe",
+    "5726ed6cf1498d1400e8f00d": "those",
+    "5726ed6cf1498d1400e8f00e": "potentially dangerous",
+    "5726ed6cf1498d1400e8f00f": "sell prescription drugs without requiring a prescription",
+    "5726ed6cf1498d1400e8f010": "sell prescription drugs and require a valid prescription",
+    "5726edecdd62a815002e957e": "ease with which people, youth in particular, can obtain controlled substances",
+    "5726edecdd62a815002e957f": "it must be issued for a legitimate medical purpose by a licensed practitioner acting in the course of legitimate doctor-patient relationship",
+    "5726ef73f1498d1400e8f05e": "ease with which people, youth in particular, can obtain controlled substances",
+    "5726ef73f1498d1400e8f05f": "it must be issued for a legitimate medical purpose by a licensed practitioner acting in the course of legitimate doctor-patient relationship",
+    "5726ef73f1498d1400e8f060": "to ensure that the prescription is valid",
+    "5726ef73f1498d1400e8f061": "individual state laws",
+    "5726ef73f1498d1400e8f062": "Vicodin",
+    "5726f0e35951b619008f82f7": "to reduce consumer costs",
+    "5726f0e35951b619008f82f8": "Canada",
+    "5726f0e35951b619008f82f9": "international drug suppliers",
+    "5726f0e35951b619008f82fa": "There is no known case of any U.S. citizens buying Canadian drugs for personal use with a prescription, who has ever been charged by authorities.",
+    "5726f0e35951b619008f82fb": "legalize importation of medications from Canada and other countries",
+    "5726f2375951b619008f830f": "pharmacy practice science and applied information science",
+    "5726f2375951b619008f8310": "information technology departments",
+    "5726f2375951b619008f8311": "major national and international patient information projects and health system interoperability goals",
+    "5726f2375951b619008f8312": "practice areas",
+    "5726f2375951b619008f8313": "quickly",
+    "5726f36cdd62a815002e95fe": "specialty drugs",
+    "5726f36cdd62a815002e95ff": "19",
+    "5726f36cdd62a815002e9600": "cancer, hepatitis, and rheumatoid arthritis",
+    "5726f36cdd62a815002e9601": "high cost injectable, oral, infused, or inhaled",
+    "5726f36cdd62a815002e9602": "lab monitoring, adherence counseling",
+    "5726f48df1498d1400e8f0da": "separately from physicians",
+    "5726f48df1498d1400e8f0db": "pharmacists",
+    "5726f48df1498d1400e8f0dc": "American Medical Association (AMA) Code of Ethics",
+    "5726f48df1498d1400e8f0dd": "7 to 10 percent",
+    "5726f48df1498d1400e8f0de": "form business partnerships with physicians or give them \"kickback\" payments",
+    "5726f635dd62a815002e9657": "United Kingdom",
+    "5726f635dd62a815002e9656": "rural areas",
+    "5726f635dd62a815002e9658": "1.6 kilometres",
+    "5726f635dd62a815002e9659": "1.6 kilometres",
+    "5726f7715951b619008f838d": "high risk of a conflict of interest and/or the avoidance of absolute powers",
+    "5726f7715951b619008f838e": "financial self-interest",
+    "5726f7715951b619008f838f": "checks and balances system of the U.S. and many other governments",
+    "5726f7715951b619008f8391": "obtaining cost-effective medication",
+    "5726f7715951b619008f8390": "obtaining cost-effective medication",
+    "5726f868dd62a815002e9684": "more integral within the health care system",
+    "5726f868dd62a815002e9685": "health care system",
+    "5726f868dd62a815002e9686": "clinical services that pharmacists can provide for their patients",
+    "5726f868dd62a815002e9687": "prescription, non-prescription, and herbals",
+    "5726f868dd62a815002e9688": "increased patient health outcomes and decreased costs to the health care system",
+    "5726f96ddd62a815002e9698": "Alberta and British Columbia",
+    "5726f96ddd62a815002e9699": "the Australian Government",
+    "5726f96ddd62a815002e969a": "medicine use reviews",
+    "5726f96ddd62a815002e969b": "pharmaceutical care or clinical pharmacy",
+    "5726f96ddd62a815002e969c": "Doctor of Pharmacy",
+    "5726fa525951b619008f83f7": "the mortar and pestle and the \u211e (recipere) character",
+    "5726fa525951b619008f83f8": "The show globe",
+    "5726fa525951b619008f83f9": "Netherlands",
+    "5726fa525951b619008f83fa": "Germany and Austria",
+    "5726fa525951b619008f83fb": "France, Argentina, the United Kingdom, Belgium, Ireland, Italy, Spain, and India",
+    "5726e65e708984140094d53d": "a system of many biological structures and processes within an organism that protects against disease",
+    "5726e65e708984140094d53e": "disease",
+    "5726e65e708984140094d53f": "innate immune system versus the adaptive immune system, or humoral immunity versus cell-mediated immunity",
+    "5726e65e708984140094d540": "neuroimmune system",
+    "5726eb76f1498d1400e8efda": "a system of many biological structures and processes within an organism that protects against disease",
+    "5726eb76f1498d1400e8efdb": "disease",
+    "5726eb76f1498d1400e8efdc": "innate immune system versus the adaptive immune system, or humoral immunity versus cell-mediated immunity",
+    "5728eff82ca10214002daadc": "disease",
+    "5728eff82ca10214002daadd": "pathogens",
+    "5728eff82ca10214002daade": "neuroimmune system",
+    "5728eff82ca10214002daadf": "the blood\u2013brain barrier, blood\u2013cerebrospinal fluid barrier, and similar fluid\u2013brain barriers",
+    "572a14af3f37b319004786c1": "pathogens",
+    "572a14af3f37b319004786c3": "innate immune system versus the adaptive immune system, or humoral immunity versus cell-mediated immunity",
+    "572a14af3f37b319004786c2": "humoral immunity versus cell-mediated immunity",
+    "572a14af3f37b319004786c4": "neuroimmune system",
+    "5726eb785951b619008f8275": "rapidly evolve and adapt",
+    "5726eb785951b619008f8276": "rudimentary",
+    "5726eb785951b619008f8277": "eukaryotes and remain in their modern descendants, such as plants and invertebrates",
+    "5726eb785951b619008f8278": "vaccination",
+    "572a135daf94a219006aa79f": "bacteriophage",
+    "572a135daf94a219006aa7a0": "defensins",
+    "572a135daf94a219006aa7a1": "vaccination",
+    "572a135daf94a219006aa7a2": "Adaptive",
+    "5726eb8bf1498d1400e8efe2": "autoimmune diseases, inflammatory diseases and cancer",
+    "5726eb8bf1498d1400e8efe3": "when the immune system is less active than normal",
+    "5726eb8bf1498d1400e8efe4": "recurring and life-threatening infections",
+    "5726eb8bf1498d1400e8efe5": "severe combined immunodeficiency, acquired conditions such as HIV/AIDS, or the use of immunosuppressive medication",
+    "5726eb8bf1498d1400e8efe6": "Hashimoto's thyroiditis",
+    "5728f2e26aef051400154896": "Immunodeficiency",
+    "5728f2e26aef051400154897": "autoimmunity",
+    "5728f2e26aef051400154898": "Immunology",
+    "5728f2e26aef051400154899": "HIV/AIDS",
+    "5726f8abf1498d1400e8f166": "the plague of Athens",
+    "5726f8abf1498d1400e8f167": "scorpion",
+    "5726f8abf1498d1400e8f168": "Louis Pasteur",
+    "5726f8abf1498d1400e8f169": "Walter Reed",
+    "5728f3724b864d1900165116": "Robert Koch",
+    "5728f3724b864d1900165117": "microorganisms",
+    "5728f3724b864d1900165118": "yellow fever virus",
+    "5728f3724b864d1900165119": "430 BC",
+    "5726f8aef1498d1400e8f16e": "immunological memory",
+    "5726f8aef1498d1400e8f16f": "innate immune system",
+    "5726f8aef1498d1400e8f170": "adaptive immune system",
+    "5728f6446aef0514001548e4": "Innate",
+    "5728f6446aef0514001548e5": "adaptive immune system",
+    "5728f6446aef0514001548e6": "an immunological memory",
+    "5728f6446aef0514001548e7": "physical barriers",
+    "5728f7774b864d190016512e": "self and non-self molecules",
+    "5728f7774b864d190016512f": "self molecules",
+    "5728f7774b864d1900165130": "non-self molecules",
+    "5728f7774b864d1900165131": "antigens",
+    "5728f7774b864d1900165132": "specific immune receptors",
+    "5728f9882ca10214002dab5a": "pattern recognition receptors",
+    "5728f9882ca10214002dab5b": "innate immune system",
+    "5728f9882ca10214002dab5c": "microorganisms",
+    "5728f9882ca10214002dab5d": "non-specific",
+    "5728fb002ca10214002dab6a": "the exoskeleton",
+    "5728fb002ca10214002dab6b": "waxy cuticle",
+    "5728fb002ca10214002dab6c": "coughing and sneezing",
+    "5728fb002ca10214002dab6d": "mucus",
+    "5728fb002ca10214002dab6e": "tears and urine",
+    "5728fc2eaf94a219006a9ec7": "\u03b2-defensins",
+    "5728fc2eaf94a219006a9ec8": "lysozyme and phospholipase A2",
+    "5728fc2eaf94a219006a9ec9": "defensins and zinc",
+    "5728fc2eaf94a219006a9eca": "gastric acid and proteases",
+    "5728fc2eaf94a219006a9ecb": "menarche",
+    "5728fd206aef05140015494c": "commensal flora",
+    "5728fd206aef05140015494d": "fungi",
+    "5728fd206aef05140015494e": "lactobacilli",
+    "5728fd206aef05140015494f": "pH or available iron",
+    "572900f73f37b31900477f69": "Inflammation",
+    "572900f73f37b31900477f6a": "increased blood flow into tissue",
+    "572900f73f37b31900477f6b": "eicosanoids and cytokines",
+    "572900f73f37b31900477f6c": "prostaglandins",
+    "572900f73f37b31900477f6d": "interleukins",
+    "572905ce1d04691400778f83": "phagocytes",
+    "572905ce1d04691400778f84": "cytokines",
+    "572905ce1d04691400778f85": "phagosome",
+    "572905ce1d04691400778f86": "phagolysosome",
+    "572905ce1d04691400778f87": "acquiring nutrients",
+    "5729081d3f37b31900477fab": "Neutrophils and macrophages",
+    "5729081d3f37b31900477fac": "Neutrophils",
+    "5729081d3f37b31900477fad": "50% to 60%",
+    "5729081d3f37b31900477fae": "chemotaxis",
+    "5729081d3f37b31900477faf": "interleukin 1",
+    "572908c13f37b31900477fbd": "Leukocytes",
+    "572908c13f37b31900477fbe": "Leukocytes",
+    "572908c13f37b31900477fbf": "adaptive",
+    "572908c13f37b31900477fc0": "macrophages, neutrophils, and dendritic cells",
+    "5729efab3f37b319004785cf": "Dendritic cells",
+    "5729efab3f37b319004785d0": "neuronal dendrites",
+    "5729efab3f37b319004785d1": "T cells",
+    "5729efab3f37b319004785d2": "T cells",
+    "5729f06f1d04691400779674": "missing self",
+    "5729f06f1d04691400779673": "Natural killer cells",
+    "5729f06f1d04691400779675": "MHC I",
+    "5729f06f1d04691400779676": "killer cell immunoglobulin receptors",
+    "5729f12e3f37b319004785e1": "early vertebrates",
+    "5729f12e3f37b319004785e2": "antigen presentation",
+    "5729f12e3f37b319004785e3": "pathogens or pathogen-infected cells",
+    "5729f1fcaf94a219006aa6d3": "killer T cell and the helper T cell",
+    "5729f1fcaf94a219006aa6d4": "regulatory T cells",
+    "5729f1fcaf94a219006aa6d5": "Class I MHC",
+    "5729f1fcaf94a219006aa6d6": "Class II MHC",
+    "5729f1fcaf94a219006aa6d7": "\u03b3\u03b4",
+    "5729f39a6aef05140015514c": "Killer",
+    "5729f39a6aef05140015514e": "CD8",
+    "5729f39a6aef05140015514d": "TCR",
+    "5729f39a6aef051400155150": "granulysin",
+    "5729f39a6aef05140015514f": "perforin",
+    "5729f4b41d0469140077968b": "CD4",
+    "5729f4b41d0469140077968c": "200\u2013300",
+    "5729f4b41d0469140077968d": "200\u2013300",
+    "5729f4b41d0469140077968e": "cytokines",
+    "5729f4b41d0469140077968f": "CD40",
+    "5729f5a03f37b31900478605": "helper T cells, cytotoxic T cells and NK cells",
+    "5729f5a03f37b31900478606": "TCR",
+    "5729f5a03f37b31900478607": "\u03b3\u03b4 T cells",
+    "5729f5a03f37b31900478608": "receptor diversity",
+    "5729f5a03f37b31900478609": "V\u03b39/V\u03b42 T cells",
+    "5729f69caf94a219006aa6f9": "B cell",
+    "5729f69caf94a219006aa6fa": "proteolysis",
+    "5729f69caf94a219006aa6fb": "lymphokines",
+    "5729f799af94a219006aa707": "long-lived memory cells",
+    "5729f799af94a219006aa708": "adaptive",
+    "5729f799af94a219006aa709": "passive short-term memory or active long-term memory",
+    "5729f799af94a219006aa70a": "pathogen",
+    "5729f8516aef05140015516c": "microbes",
+    "5729f8516aef05140015516d": "IgG",
+    "5729f8516aef05140015516e": "Breast milk or colostrum",
+    "5729f8516aef05140015516f": "passive",
+    "5729f9953f37b3190047861f": "immunomodulators",
+    "5729f9953f37b31900478620": "adaptive and innate",
+    "5729f9953f37b31900478621": "lupus erythematosus",
+    "5729f9953f37b31900478622": "immunosuppressive",
+    "5729fa40af94a219006aa70f": "NFIL3",
+    "5729fa40af94a219006aa710": "heart disease, chronic pain, and asthma",
+    "5729fa40af94a219006aa711": "sleep deprivation",
+    "5729fb003f37b31900478627": "a progressive decline in hormone levels with age",
+    "5729fb003f37b31900478628": "vitamin D",
+    "5729fb003f37b31900478629": "thyroid hormone activity",
+    "5729fb003f37b3190047862a": "cholecalciferol",
+    "5729fc3d1d0469140077969d": "killer T cells",
+    "5729fc3d1d0469140077969e": "MHC class I",
+    "5729fc3d1d0469140077969f": "viral antigens",
+    "5729fc3d1d046914007796a0": "antibodies",
+    "5729fd111d046914007796a5": "phagocytic",
+    "5729fd111d046914007796a6": "Pathogen-associated molecular patterns or PAMPs",
+    "5729fd111d046914007796a7": "rapid apoptosis",
+    "5729fd111d046914007796a8": "Systemic acquired resistance",
+    "5729fd111d046914007796a9": "RNA silencing",
+    "5729fe5c3f37b3190047862f": "autoimmune disorders",
+    "5729fe5c3f37b31900478630": "self and non-self",
+    "5729fe5c3f37b31900478631": "thymus and bone marrow",
+    "5729fe5c3f37b31900478632": "self\" peptides",
+    "5729fefbaf94a219006aa739": "Immunodeficiencies",
+    "5729fefbaf94a219006aa73a": "young and the elderly",
+    "5729fefbaf94a219006aa73b": "50",
+    "5729fefbaf94a219006aa73c": "obesity, alcoholism, and drug use",
+    "5729fefbaf94a219006aa73d": "malnutrition",
+    "5729ffda1d046914007796af": "vaccination",
+    "5729ffda1d046914007796b0": "immunization",
+    "5729ffda1d046914007796b1": "an antigen from a pathogen",
+    "5729ffda1d046914007796b2": "natural specificity",
+    "572a019f3f37b31900478643": "enzymes that digest the barrier, for example, by using a type II",
+    "572a019f3f37b31900478644": "type III",
+    "572a019f3f37b31900478645": "shut down host defenses",
+    "572a019f3f37b31900478646": "elude host immune responses",
+    "572a02483f37b3190047864b": "Frank Burnet",
+    "572a02483f37b3190047864c": "pathogens, an allograft",
+    "572a02483f37b3190047864d": "histocompatibility",
+    "572a02483f37b3190047864e": "Niels Jerne",
+    "572a03086aef0514001551a2": "Glucocorticoids",
+    "572a03086aef0514001551a3": "cytotoxic or immunosuppressive",
+    "572a03086aef0514001551a4": "methotrexate or azathioprine",
+    "572a03086aef0514001551a5": "cyclosporin",
+    "572a04d51d046914007796cd": "cytotoxic natural killer cells and CTLs",
+    "572a04d51d046914007796ce": "cortisol and catecholamines",
+    "572a04d51d046914007796cf": "melatonin",
+    "572a04d51d046914007796d0": "free radical",
+    "572a058aaf94a219006aa751": "vitamin D receptor",
+    "572a058aaf94a219006aa752": "calcitriol",
+    "572a058aaf94a219006aa753": "symbiotic",
+    "572a058aaf94a219006aa754": "CYP27B1",
+    "572a058aaf94a219006aa755": "dendritic cells, keratinocytes and macrophages",
+    "572a06af3f37b31900478667": "Pattern recognition receptors",
+    "572a06af3f37b31900478668": "defensins",
+    "572a06af3f37b31900478669": "phagocytic",
+    "572a06af3f37b3190047866a": "Ribonucleases and the RNA interference pathway",
+    "572a07fc6aef0514001551dc": "immunoglobulins and T cell receptors",
+    "572a07fc6aef0514001551dd": "lamprey and hagfish",
+    "572a07fc6aef0514001551de": "Variable lymphocyte receptors",
+    "572a07fc6aef0514001551df": "adaptive immune system",
+    "572a096e6aef0514001551e4": "lymphocytes",
+    "572a096e6aef0514001551e5": "restriction modification system",
+    "572a096e6aef0514001551e6": "viral",
+    "572a096e6aef0514001551e7": "CRISPR sequences",
+    "572a0a686aef0514001551ec": "\"cellular\" and \"humoral",
+    "572a0a686aef0514001551ed": "Elie Metchnikoff",
+    "572a0a686aef0514001551ee": "phagocytes",
+    "572a0a686aef0514001551ef": "Robert Koch and Emil von Behring",
+    "572a0a686aef0514001551f0": "soluble components (molecules) found in the organism\u2019s \u201chumors",
+    "572a0b0b6aef0514001551f6": "cancers",
+    "572a0b0b6aef0514001551f7": "MHC class I molecules",
+    "572a0b0b6aef0514001551f8": "cytokine TGF-\u03b2",
+    "572a0b0b6aef0514001551f9": "macrophages and lymphocytes",
+    "572a0bf96aef051400155204": "Hypersensitivity",
+    "572a0bf96aef051400155205": "four",
+    "572a0bf96aef051400155206": "Type I hypersensitivity",
+    "572a0bf96aef051400155207": "IgE",
+    "572a0bf96aef051400155208": "cytotoxic",
+    "572a0ce11d046914007796fd": "intracellular pathogenesis",
+    "572a0ce11d046914007796fe": "Salmonella",
+    "572a0ce11d046914007796ff": "Plasmodium falciparum",
+    "572a0ce11d04691400779700": "Mycobacterium tuberculosis",
+    "572a0ce11d04691400779701": "protein A",
+    "572a0e4b6aef051400155214": "antigenic variation",
+    "572a0e4b6aef051400155215": "HIV",
+    "572a0e4b6aef051400155216": "Trypanosoma brucei",
+    "572a0e4b6aef051400155217": "antigens",
+    "572a0f073f37b3190047867f": "immune surveillance",
+    "572a0f073f37b31900478680": "papillomavirus",
+    "572a0f073f37b31900478681": "tyrosinase",
+    "572a0f073f37b31900478682": "melanomas",
+    "572a0f073f37b31900478683": "melanomas",
+    "572a10cd6aef051400155222": ">500 Da",
+    "572a10cd6aef051400155223": "hydrophilic",
+    "572a10cd6aef051400155224": "Immunoproteomics",
+    "572a10cd6aef051400155225": "B",
+    "572a10cd6aef051400155226": "immunoinformatics",
+    "572a12386aef051400155234": "leptin, pituitary growth hormone, and prolactin",
+    "572a12386aef051400155235": "APCs",
+    "572a12386aef051400155236": "Th1/Th2",
+    "572a12386aef051400155237": "Th1",
+    "572a142e3f37b319004786b9": "carbohydrates",
+    "572a142e3f37b319004786bc": "disrupting their plasma membrane",
+    "572a142e3f37b319004786ba": "signal amplification",
+    "572a142e3f37b319004786bb": "catalytic",
+    "57271c235951b619008f860b": "Civil disobedience",
+    "57271c235951b619008f860c": "fight against apartheid",
+    "57271c235951b619008f860d": "Singing Revolution",
+    "57271c235951b619008f860e": "Ukraine",
+    "57271c235951b619008f860f": "Georgia",
+    "5728d4c03acd2414000dff9f": "Egyptians",
+    "5728d4c03acd2414000dffa0": "British occupation",
+    "5728d4c03acd2414000dffa1": "unfair laws",
+    "5728d4c03acd2414000dffa2": "unfair laws",
+    "5728d4c03acd2414000dffa3": "Singing Revolution",
+    "57271f125951b619008f8635": "Antigone",
+    "57271f125951b619008f8636": "Oedipus",
+    "57271f125951b619008f8637": "Creon",
+    "57271f125951b619008f8638": "Sophocles",
+    "57271f125951b619008f8639": "giving her brother Polynices a proper burial",
+    "5728d5793acd2414000dffb3": "Antigone",
+    "5728d5793acd2414000dffb4": "Sophocles",
+    "5728d5793acd2414000dffb5": "Oedipus",
+    "5728d5793acd2414000dffb6": "she must obey her conscience rather than human law",
+    "5728d5793acd2414000dffb7": "one of the daughters of former King of Thebes, Oedipus, defies Creon",
+    "5727213c708984140094da35": "Percy Shelley",
+    "5727213c708984140094da36": "nonviolent",
+    "5727213c708984140094da38": "Satyagraha",
+    "5727213c708984140094da39": "a free India",
+    "5727213c708984140094da37": "Henry David Thoreau",
+    "5728d63c4b864d1900164f18": "Percy Shelley",
+    "5728d63c4b864d1900164f19": "the unjust forms of authority",
+    "5728d63c4b864d1900164f1a": "nonviolent protest",
+    "5728d63c4b864d1900164f1c": "Satyagraha",
+    "5728d63c4b864d1900164f1b": "Henry David Thoreau",
+    "572726c9708984140094da7b": "muggers, arsonists, draft evaders, campaign hecklers, campus militants, anti-war demonstrators, juvenile delinquents and political assassins",
+    "572726c9708984140094da7e": "Marshall Cohen",
+    "572726c9708984140094da7c": "ambiguity",
+    "572726c9708984140094da7d": "utterly debased",
+    "5728d6f02ca10214002da90e": "utterly debased",
+    "5728d6f02ca10214002da90f": "Marshall Cohen",
+    "5728d6f02ca10214002da910": "utterly debased",
+    "5728d6f02ca10214002da911": "Marshall Cohen",
+    "5728d6f02ca10214002da912": "ambiguity",
+    "572807802ca10214002d9bf8": "LeGrande",
+    "572807802ca10214002d9bf9": "extremely difficult, if not impossible",
+    "572807802ca10214002d9bfa": "lawful protest demonstration, nonviolent civil disobedience, and violent civil disobedience",
+    "572807802ca10214002d9bfb": "semantical",
+    "572807802ca10214002d9bfc": "no more (or no less) meaning",
+    "5728d7c54b864d1900164f4c": "LeGrande",
+    "5728d7c54b864d1900164f4d": "voluminous",
+    "5728d7c54b864d1900164f4e": "semantical problems and grammatical niceties",
+    "5728d7c54b864d1900164f4f": "lawful protest demonstration, nonviolent",
+    "5728d7c54b864d1900164f50": "violent",
+    "57280f974b864d1900164370": "civil disobedience",
+    "57280f974b864d1900164371": "private citizen",
+    "57280f974b864d1900164372": "relation to the state and its laws",
+    "57280f974b864d1900164373": "if the head of government of a country were to refuse to enforce a decision of that country's highest court",
+    "57280f974b864d1900164374": "Civil disobedience",
+    "5728d9403acd2414000e001b": "a citizen's relation to the state and its laws",
+    "5728d9403acd2414000e001c": "if the head of government of a country were to refuse to enforce a decision of that country's highest court",
+    "5728d9403acd2414000e001d": "head of government",
+    "5728d9403acd2414000e001e": "citizen",
+    "5728d9403acd2414000e001f": "private citizen",
+    "572812e74b864d19001643ce": "Thoreau",
+    "572812e74b864d19001643cf": "Resign",
+    "572812e74b864d19001643d0": "powerful but it is not necessarily right",
+    "572812e74b864d19001643d1": "Resign",
+    "572812e74b864d19001643d2": "a postman or tax collector",
+    "5728dafe3acd2414000e005d": "The individual",
+    "5728dafe3acd2414000e005e": "individuals",
+    "5728dafe3acd2414000e005f": "Thoreau",
+    "5728dafe3acd2414000e0060": "Resign",
+    "5728dafe3acd2414000e0061": "The majority may be powerful",
+    "5728151b4b864d1900164428": "governmental entities",
+    "5728151b4b864d1900164429": "trade unions, banks, and private universities",
+    "5728151b4b864d190016442a": "governmental",
+    "5728151b4b864d190016442b": "governmental entities",
+    "5728dc2d3acd2414000e0080": "Brownlee",
+    "5728dc2d3acd2414000e0081": "a larger challenge to the legal system",
+    "5728dc2d3acd2414000e007f": "justified against governmental entities",
+    "5728dc2d3acd2414000e0083": "governmental",
+    "572818f54b864d190016446c": "civil disobedience",
+    "572818f54b864d190016446d": "covert lawbreaking",
+    "572818f54b864d190016446e": "hiding a Jew in their house",
+    "572818f54b864d190016446f": "Book of Exodus",
+    "572818f54b864d1900164470": "Shiphrah and Puah",
+    "5728dddc2ca10214002da9d2": "publicly announced",
+    "5728dddc2ca10214002da9d3": "rules that conflict with morality",
+    "5728dddc2ca10214002da9d4": "assisting in fabricating evidence or committing perjury",
+    "5728dddc2ca10214002da9d5": "Book of Exodus",
+    "5728dddc2ca10214002da9d6": "Book of Exodus",
+    "57281c594b864d19001644a2": "non-violent",
+    "57281c594b864d19001644a3": "Black's Law Dictionary",
+    "57281c594b864d19001644a4": "rebellion",
+    "57281c594b864d19001644a5": "tolerance",
+    "57281c594b864d19001644a6": "non-violent",
+    "5728df634b864d1900164fe2": "non-violent. Black's Law Dictionary includes non-violence",
+    "5728df634b864d1900164fe3": "non-violence",
+    "5728df634b864d1900164fe4": "one cannot justify a civil disobedients' use of force and violence and refusal to submit to arrest",
+    "5728df634b864d1900164fe6": "appeal to constitutional defects",
+    "5728202c4b864d19001644ec": "Revolutionary civil disobedience",
+    "5728202c4b864d19001644ed": "Hungarians",
+    "5728202c4b864d19001644ee": "Ferenc De\u00e1k",
+    "5728202c4b864d19001644ef": "Gandhi",
+    "5728202c4b864d19001644f0": "laws",
+    "5728e07e3acd2414000e00e9": "Non-revolutionary civil disobedience",
+    "5728e07e3acd2414000e00ea": "they are judged \"wrong\" by an individual conscience",
+    "5728e07e3acd2414000e00eb": "to overthrow a government",
+    "5728e07e3acd2414000e00ec": "Revolutionary",
+    "5728e07e3acd2414000e00ed": "Gandhi",
+    "572822233acd2414000df555": "during the Roman Empire",
+    "572822233acd2414000df556": "Unarmed Jews gathered in the streets",
+    "572822233acd2414000df557": "his arrest was not covered in any newspapers",
+    "572822233acd2414000df558": "higher political office",
+    "572822233acd2414000df559": "after the end of the Mexican War",
+    "5728e3c33acd2414000e0131": "during the Roman Empire",
+    "5728e3c33acd2414000e0132": "to prevent the installation of pagan images in the Temple in Jerusalem",
+    "5728e3c33acd2414000e0133": "jail solidarity",
+    "5728e3c33acd2414000e0134": "jail solidarity",
+    "5728e3c33acd2414000e0135": "days, weeks and months",
+    "572825a92ca10214002d9efe": "illegal acts",
+    "572825a92ca10214002d9eff": "propaganda purpose",
+    "572825a92ca10214002d9f00": "Voice in the Wilderness",
+    "572825a92ca10214002d9f01": "738 days",
+    "572825a92ca10214002d9f02": "successfully preventing it from being cut down",
+    "5728e5224b864d1900165032": "harassment",
+    "5728e5224b864d1900165033": "trespassing at a nuclear-missile installation",
+    "5728e5224b864d1900165034": "harassment",
+    "5728e5224b864d1900165035": "harassment",
+    "5728e5224b864d1900165036": "Julia Butterfly Hill",
+    "5728284e3acd2414000df5cd": "allegedly sending an email to the Lebanon, New Hampshire city councilors stating, \"Wise up or die.\"",
+    "5728284e3acd2414000df5ce": "\"Wise up or die.\"",
+    "5728284e3acd2414000df5cf": "forbidden speech",
+    "5728284e3acd2414000df5d0": "FCC v. Pacifica Foundation",
+    "5728284e3acd2414000df5d1": "1978",
+    "5728e5f43acd2414000e0171": "civil disobedience can consist simply of engaging in the forbidden speech",
+    "5728e5f43acd2414000e0172": "Threatening government officials",
+    "5728e5f43acd2414000e0173": "Threatening government officials",
+    "5728e5f43acd2414000e0174": "engaging in the forbidden speech",
+    "57282dfb4b864d1900164668": "more difficult for a system to function",
+    "57282dfb4b864d1900164669": "padlocking the gates and using sickles to deflate one of the large domes covering two satellite dishes",
+    "57282dfb4b864d190016466a": "padlocking the gates and using sickles to deflate one of the large domes covering two satellite dishes",
+    "57282dfb4b864d190016466b": "coercion",
+    "57282dfb4b864d190016466c": "coercive",
+    "5728e715ff5b5019007da914": "civil disobedience",
+    "5728e715ff5b5019007da916": "more difficult for a system to function",
+    "5728e715ff5b5019007da917": "engage in moral dialogue",
+    "5728e715ff5b5019007da918": "coercive",
+    "5728e8212ca10214002daa6c": "criminal investigations and arrests",
+    "5728e8212ca10214002daa6d": "arrest",
+    "5728e8212ca10214002daa6e": "harmful",
+    "5728e8212ca10214002daa6f": "lack of understanding of the legal ramifications, or due to a fear of seeming rude",
+    "5728e8212ca10214002daa70": "lack of understanding of the legal ramifications, or due to a fear of seeming rude",
+    "5728eb1a3acd2414000e01c5": "it is incumbent upon them to accept punishment because of their belief in the validity of the social contract",
+    "5728eb1a3acd2414000e01c6": "their belief in the validity of the social contract",
+    "5728eb1a3acd2414000e01c7": "their belief in the validity of the social contract",
+    "5728eb1a3acd2414000e01c8": "anarchists",
+    "5728eb1a3acd2414000e01c9": "a violation of criminal law that does not infringe the rights of others",
+    "5728ebcb3acd2414000e01d9": "whether or not to plead guilty",
+    "5728ebcb3acd2414000e01da": "to submit to the punishment prescribed by law",
+    "5728ebcb3acd2414000e01db": "it is a civil disobedient's duty to submit to the punishment prescribed by law",
+    "5728ebcb3acd2414000e01dc": "it is a civil disobedient's duty to submit to the punishment prescribed by law",
+    "5728ebcb3acd2414000e01dd": "not guilty",
+    "5728ec6a4b864d19001650aa": "Camp Mercury nuclear test site",
+    "5728ec6a4b864d19001650ab": "13 of the protesters attempted to enter the test site knowing that they faced arrest",
+    "5728ec6a4b864d19001650ac": "suspended sentences",
+    "5728ec6a4b864d19001650ad": "nolo contendere",
+    "5728ec6a4b864d19001650ae": "suspended",
+    "5728ed94ff5b5019007da97c": "reminding their countrymen of injustice",
+    "5728ed94ff5b5019007da97d": "reminding their countrymen of injustice",
+    "5728ed94ff5b5019007da97e": "remaining in jail, or by evading it",
+    "5728ed94ff5b5019007da97f": "civil disobedience",
+    "5728eef92ca10214002daab0": "plea bargain",
+    "5728eef92ca10214002daab1": "civil disobedients",
+    "5728eef92ca10214002daab2": "solidarity",
+    "5728eef92ca10214002daab3": "plea bargain",
+    "5728eef92ca10214002daab4": "Mohandas Gandhi",
+    "5728f50baf94a219006a9e55": "allocution",
+    "5728f50baf94a219006a9e56": "mistreatment from government officials",
+    "5728f50baf94a219006a9e57": "mistreatment from government officials",
+    "5728f50baf94a219006a9e58": "lack of remorse, an attempt to avoid responsibility for her actions, and even a likelihood of repeating her illegal actions",
+    "5728f50baf94a219006a9e59": "allocution",
+    "5728facd4b864d1900165170": "to win an acquittal and avoid imprisonment or a fine",
+    "5728facd4b864d1900165171": "win an acquittal and avoid imprisonment or a fine",
+    "5728facd4b864d1900165172": "jury nullification",
+    "5728facd4b864d1900165173": "Vietnam War",
+    "5728facd4b864d1900165174": "A technical defense",
+    "5728fb6a1d04691400778ef5": "attribution",
+    "5728fb6a1d04691400778ef6": "neither conscientious nor of social benefit",
+    "5728fb6a1d04691400778ef7": "any great amount of it would undermine the law by encouraging general disobedience which is neither conscientious nor of social benefit",
+    "5728fb6a1d04691400778ef8": "attempts to escape punishment by committing the crime covertly and avoiding attribution",
+    "5728fb6a1d04691400778ef9": "covertly and avoiding attribution",
+    "5728fc9e1d04691400778f11": "Indirect civil disobedience",
+    "5728fc9e1d04691400778f12": "civil disobedience",
+    "5728fc9e1d04691400778f13": "Vietnam War",
+    "5728fc9e1d04691400778f14": "necessity",
+    "5728fc9e1d04691400778f15": "if they arrest fully informed jury leafleters, the leaflets will have to be given to the leafleter's own jury as evidence",
+    "5728fd3c3f37b31900477f2b": "achieving crime control via incapacitation and deterrence",
+    "5728fd3c3f37b31900477f2d": "achieving crime control via incapacitation and deterrence",
+    "5728fd3c3f37b31900477f2e": "the state",
+    "5728fd3c3f37b31900477f2f": "just deserts",
+    "57273a465951b619008f86ff": "Construction",
+    "57273a465951b619008f8700": "manufacturing",
+    "57273a465951b619008f8701": "six to nine percent",
+    "57273a465951b619008f8702": "design, and financing",
+    "57273a465951b619008f8703": "a known client",
+    "57273cca708984140094db33": "An architect",
+    "57273cca708984140094db34": "construction manager, design engineer, construction engineer or project manager",
+    "57273cca708984140094db35": "effective planning",
+    "57273cca708984140094db36": "megaprojects",
+    "57273cca708984140094db37": "Those involved with the design and execution of the infrastructure in question",
+    "57273d8bf1498d1400e8f4fe": "buildings, infrastructure and industrial",
+    "57273d8bf1498d1400e8f4ff": "residential and non-residential (commercial/institutional)",
+    "57273d8bf1498d1400e8f500": "heavy/highway, heavy civil or heavy engineering",
+    "57273d8bf1498d1400e8f501": "Infrastructure",
+    "57273d8bf1498d1400e8f502": "Industrial",
+    "57273e50dd62a815002e9a02": "a trade magazine for the construction industry",
+    "57273e50dd62a815002e9a03": "Engineering News-Record",
+    "57273e50dd62a815002e9a04": "2014",
+    "57273e50dd62a815002e9a05": "transportation, sewer, hazardous waste and water",
+    "57273f27dd62a815002e9a0a": "building construction, heavy and civil engineering construction, and specialty trade contractors",
+    "57273f27dd62a815002e9a0b": "construction service firms",
+    "57273f27dd62a815002e9a0c": "The Standard Industrial Classification and the newer North American Industry Classification System",
+    "57273f27dd62a815002e9a0d": "firms engaged in managing construction projects without assuming direct financial responsibility for completion of the construction project",
+    "57274046dd62a815002e9a12": "Building construction",
+    "57274046dd62a815002e9a13": "small renovations",
+    "57274046dd62a815002e9a14": "the owner of the property",
+    "57274046dd62a815002e9a15": "structural collapse, cost overruns, and/or litigation",
+    "57274046dd62a815002e9a16": "make detailed plans and maintain careful oversight",
+    "572742bd5951b619008f8785": "local building authority regulations and codes of practice",
+    "572742bd5951b619008f8786": "Materials readily available in the area",
+    "572742bd5951b619008f8787": "waste",
+    "572742bd5951b619008f8788": "Cost of construction on a per square meter (or per square foot) basis for houses",
+    "572744aff1498d1400e8f588": "3D printing technology",
+    "572744aff1498d1400e8f589": "20 hours",
+    "572744aff1498d1400e8f58a": "2014",
+    "572744aff1498d1400e8f58b": "2 metres (6 ft 7 in)",
+    "572745c6708984140094db9a": "plan the physical proceedings",
+    "572745c6708984140094db99": "designs",
+    "572745c6708984140094db9b": "the property owner",
+    "572745c6708984140094db9c": "quantity surveyor",
+    "572745c6708984140094db9d": "the most cost efficient bidder",
+    "57274e0d708984140094dbe5": "previously separated specialties",
+    "57274e0d708984140094dbe6": "entirely separate companies",
+    "57274e0d708984140094dbe7": "one-stop shopping",
+    "57274e0d708984140094dbe8": "design build\" contract",
+    "57274e975951b619008f87f9": "design-build, partnering and construction management",
+    "57274e975951b619008f87fa": "architects, interior designers, engineers and constructors",
+    "57274e975951b619008f87fb": "establishing relationships with other necessary participants",
+    "57274f67708984140094dbf5": "preventable financial problems",
+    "57274f67708984140094dbf6": "when builders ask for too little money to complete the project",
+    "57274f67708984140094dbf7": "when the present amount of funding cannot cover the current costs for labour and materials",
+    "57274f67708984140094dbf8": "Fraud",
+    "5727502f708984140094dc07": "Mortgage bankers, accountants, and cost engineers",
+    "5727502f708984140094dc08": "the mortgage banker",
+    "5727502f708984140094dc09": "Accountants",
+    "5727502f708984140094dc0b": "identified change orders",
+    "5727502f708984140094dc0a": "Cost engineers and estimators",
+    "572750e8dd62a815002e9af0": "zoning and building code requirements",
+    "572750e8dd62a815002e9af1": "the owner",
+    "572750e8dd62a815002e9af2": "the desire to prevent things that are indisputably bad \u2013 bridge collapses or explosions",
+    "572750e8dd62a815002e9af3": "things that are a matter of custom or expectation",
+    "572750e8dd62a815002e9af4": "An attorney",
+    "572751b4708984140094dc1b": "A construction project",
+    "572751b4708984140094dc1c": "A contract",
+    "572751b4708984140094dc1d": "a delay costs money",
+    "572751b4708984140094dc1e": "that each side is capable of performing the obligations set out",
+    "572751b4708984140094dc1f": "poorly drafted contracts",
+    "5727526cdd62a815002e9b0e": "relationship contracting",
+    "5727526cdd62a815002e9b0f": "Public-Private Partnering",
+    "5727526cdd62a815002e9b10": "Public-Private Partnering",
+    "5727526cdd62a815002e9b11": "co-operation",
+    "572753335951b619008f8853": "the architect or engineer",
+    "572753335951b619008f8854": "the architect or engineer",
+    "572753335951b619008f8855": "the architect's client and the main contractor",
+    "572753335951b619008f8856": "the main contractor",
+    "572753335951b619008f8857": "the building is ready to occupy",
+    "57275411dd62a815002e9b30": "The owner",
+    "57275411dd62a815002e9b31": "D&B contractors",
+    "57275411dd62a815002e9b32": "The owner",
+    "57275411dd62a815002e9b33": "a consortium of several contractors",
+    "57275411dd62a815002e9b34": "they design phase 2",
+    "572754fff1498d1400e8f660": "contractors",
+    "572754fff1498d1400e8f661": "damage",
+    "572754fff1498d1400e8f662": "electrical, water, sewage, phone, and cable",
+    "572754fff1498d1400e8f663": "the municipal building inspector",
+    "572754fff1498d1400e8f664": "an occupancy permit",
+    "572755b7708984140094dc4d": "$960 billion",
+    "572755b7708984140094dc4e": "$680 billion",
+    "572755b7708984140094dc4f": "667,000",
+    "572755b7708984140094dc50": "fewer than 10",
+    "572755b7708984140094dc51": "828,000",
+    "5727565ef1498d1400e8f674": "\u00a342,090",
+    "5727565ef1498d1400e8f675": "\u00a326,719",
+    "5727565ef1498d1400e8f676": "US/Canada",
+    "572756fe708984140094dc71": "Construction",
+    "572756fe708984140094dc72": "Falls",
+    "572756fe708984140094dc73": "electrocution, transportation accidents, and trench cave-ins",
+    "572756fe708984140094dc74": "Proper safety equipment",
+    "572746addd62a815002e9a5e": "independent schools",
+    "572746addd62a815002e9a5f": "academic scholarship",
+    "572746addd62a815002e9a60": "public (government) funding",
+    "572746addd62a815002e9a61": "the right to select their students",
+    "57274712708984140094dbad": "$45,000",
+    "57274712708984140094dbae": "tuition-free",
+    "57274712708984140094dbaf": "Australia",
+    "57274712708984140094dbb0": "North America",
+    "5727478cf1498d1400e8f59a": "lower sixth",
+    "5727478cf1498d1400e8f59b": "upper sixth",
+    "5727478cf1498d1400e8f59c": "prep schools",
+    "5727478cf1498d1400e8f59d": "peer tuitions",
+    "5727478cf1498d1400e8f59e": "best teachers",
+    "5727482bf1498d1400e8f5a4": "Roman Catholic",
+    "5727482bf1498d1400e8f5a5": "Orthodox Christians",
+    "5727482bf1498d1400e8f5a6": "academic subjects",
+    "5727490bdd62a815002e9a82": "power of expulsion",
+    "5727490bdd62a815002e9a83": "a compulsory blazer",
+    "5727490bdd62a815002e9a84": "more expensive",
+    "57274971708984140094dbbb": "Presbyterian Church",
+    "57274971708984140094dbbc": "Catholic",
+    "57274971708984140094dbbd": "Sydney",
+    "57274971708984140094dbbe": "girls",
+    "57274a1edd62a815002e9a9a": "Article 7, Paragraph 4",
+    "57274a1edd62a815002e9a9b": "a second Gleichschaltung",
+    "57274a1edd62a815002e9a9c": "11.1%",
+    "57274a1edd62a815002e9a9d": "11.1%",
+    "57274a1edd62a815002e9a9e": "11.1%",
+    "57274a8ff1498d1400e8f5bc": "Sonderungsverbot",
+    "57274a8ff1498d1400e8f5bd": "Ersatzschulen",
+    "57274a8ff1498d1400e8f5be": "low tuition fees and/or offer scholarships",
+    "57274b35f1498d1400e8f5d4": "Erg\u00e4nzungsschulen",
+    "57274b35f1498d1400e8f5d5": "vocational schools",
+    "57274b35f1498d1400e8f5d6": "charging their students tuition fees",
+    "57274b35f1498d1400e8f5d7": "religious",
+    "57274beff1498d1400e8f5e4": "independent schools",
+    "57274beff1498d1400e8f5e5": "CBSE",
+    "57274beff1498d1400e8f5e6": "30",
+    "57274beff1498d1400e8f5e7": "the union government",
+    "57274ca8dd62a815002e9aa4": "societies",
+    "57274ca8dd62a815002e9aa5": "India",
+    "57274ca8dd62a815002e9aa6": "Annual Status of Education Report",
+    "57274ca8dd62a815002e9aa7": "evaluates learning levels in rural India",
+    "57274ca8dd62a815002e9aa8": "English",
+    "57274d1cdd62a815002e9aae": "scoil phr\u00edobh\u00e1ideach",
+    "57274d1cdd62a815002e9aaf": "a certain number of teacher's salaries are paid by the State",
+    "57274d1cdd62a815002e9ab0": "\u20ac5,000",
+    "57274d1cdd62a815002e9ab1": "Society of Jesus",
+    "57274d1cdd62a815002e9ab2": "\u20ac25,000 per year",
+    "57274d9bf1498d1400e8f5f6": "1957",
+    "57274d9bf1498d1400e8f5f7": "Chinese",
+    "57274d9bf1498d1400e8f5f8": "English",
+    "57274d9bf1498d1400e8f5f9": "National School system",
+    "57274d9bf1498d1400e8f5fa": "Over 60",
+    "57274e145951b619008f87e7": "aided' schools",
+    "57274e145951b619008f87e8": "accept government funds",
+    "57274e145951b619008f87e9": "Kathmandu",
+    "57274e145951b619008f87ea": "English",
+    "57274e145951b619008f87eb": "Nepali",
+    "57274eca5951b619008f87ff": "88",
+    "57274eca5951b619008f8800": "28,000",
+    "57274eca5951b619008f8801": "3.7%",
+    "57274eca5951b619008f8802": "Catholic",
+    "57274eca5951b619008f8803": "Auckland",
+    "57274f49f1498d1400e8f61e": "Anglican",
+    "57274f49f1498d1400e8f61f": "Wellington",
+    "57274f49f1498d1400e8f620": "Presbyterian",
+    "57274f49f1498d1400e8f621": "Christchurch",
+    "57274f49f1498d1400e8f622": "Society of St Pius X",
+    "5727500f708984140094dbfd": "7.5%",
+    "5727500f708984140094dbfe": "32%",
+    "5727500f708984140094dbff": "80%",
+    "5727500f708984140094dc00": "August 1992",
+    "5727500f708984140094dc01": "natural science",
+    "572750df5951b619008f882f": "The Education Service Contracting scheme",
+    "572750df5951b619008f8830": "Tuition Fee Supplement",
+    "572750df5951b619008f8831": "Private Education Student Financial Assistance",
+    "5727515f708984140094dc11": "The South African Schools Act of 1996",
+    "5727515f708984140094dc12": "1996",
+    "5727515f708984140094dc13": "state-controlled",
+    "5727515f708984140094dc14": "private schools",
+    "5727515f708984140094dc15": "early nineteenth",
+    "57275409708984140094dc35": "private schools, as they are state-controlled. All schools in South Africa (including both independent schools and public schools",
+    "57275409708984140094dc36": "better",
+    "57275409708984140094dc37": "higher school fees",
+    "572754dd708984140094dc3b": "Over 10%",
+    "572754dd708984140094dc3c": "10,000",
+    "572754dd708984140094dc3d": "700",
+    "572754dd708984140094dc3e": "The Knowledge School",
+    "572754dd708984140094dc3f": "school voucher",
+    "572756265951b619008f886d": "13",
+    "572756265951b619008f886e": "public schools",
+    "572756265951b619008f886f": "9",
+    "572756265951b619008f8870": "13",
+    "572756265951b619008f8871": "under \u00a33,000 to \u00a321,000",
+    "572757bef1498d1400e8f690": "Brown v. Board of Education of Topeka",
+    "572757bef1498d1400e8f691": "segregation academies",
+    "572757bef1498d1400e8f692": "South",
+    "572757bef1498d1400e8f693": "white",
+    "572757bef1498d1400e8f694": "African-American",
+    "572758e0f1498d1400e8f6ae": "religious organizations or private individuals",
+    "572758e0f1498d1400e8f6af": "First Amendment",
+    "572758e0f1498d1400e8f6b0": "Blaine Amendments",
+    "572758e0f1498d1400e8f6b1": "charter",
+    "572759665951b619008f8883": "Massachusetts",
+    "572759665951b619008f8884": "1852",
+    "572759665951b619008f8885": "1972",
+    "572759665951b619008f8886": "268 U.S. 510",
+    "572759665951b619008f8887": "Runyon v. McCrary",
+    "572759dbdd62a815002e9b94": "$40,000",
+    "572759dbdd62a815002e9b95": "$50,000",
+    "572759dbdd62a815002e9b96": "Groton School",
+    "572759dbdd62a815002e9b97": "fundraising drives",
+    "5727aa413acd2414000de921": "John Harvard",
+    "5727aa413acd2414000de922": "1977",
+    "5727aa413acd2414000de923": "James Bryant Conant",
+    "5727aa413acd2414000de924": "Association of American Universities",
+    "5727aa413acd2414000de925": "Charles W. Eliot",
+    "5727aeac3acd2414000de987": "Harvard Library",
+    "5727aeac3acd2414000de988": "79",
+    "5727aeac3acd2414000de989": "over 18 million",
+    "5727aeac3acd2414000de98a": "eight",
+    "5727aeac3acd2414000de98b": "150",
+    "5727aec03acd2414000de991": "Cambridge",
+    "5727aec03acd2414000de992": "$37.6 billion",
+    "5727aec03acd2414000de993": "Charles River",
+    "5727aec03acd2414000de994": "eleven",
+    "5727aec03acd2414000de995": "Harvard Yard",
+    "5727afa82ca10214002d93c2": "1636",
+    "5727afa82ca10214002d93c3": "the Great and General Court of the Massachusetts Bay Colony",
+    "5727afa82ca10214002d93c4": "1638",
+    "5727afa82ca10214002d93c5": "1639",
+    "5727afa82ca10214002d93c6": "1650",
+    "5727b0892ca10214002d93e8": "Puritan",
+    "5727b0892ca10214002d93e9": "English university model",
+    "5727b0892ca10214002d93ea": "It was never affiliated with any particular denomination",
+    "5727b8df3acd2414000dea99": "1804",
+    "5727b8df3acd2414000dea9a": "Samuel Webber",
+    "5727b8df3acd2414000dea9b": "1805",
+    "5727c0402ca10214002d9564": "Louis Agassiz",
+    "5727c0402ca10214002d9565": "intuition",
+    "5727c0402ca10214002d9566": "Thomas Reid and Dugald Stewart",
+    "5727c3b02ca10214002d95ba": "Charles W. Eliot",
+    "5727c3b02ca10214002d95bb": "Transcendentalist Unitarian convictions",
+    "5727c3b02ca10214002d95bc": "William Ellery Channing and Ralph Waldo Emerson",
+    "5727c55bff5b5019007d94ce": "James Bryant Conant",
+    "5727c55bff5b5019007d94cf": "higher education",
+    "5727c55bff5b5019007d94d0": "1945",
+    "5727c69d3acd2414000dec13": "four",
+    "5727c69d3acd2414000dec14": "1977",
+    "5727c69d3acd2414000dec15": "the proportion of female undergraduates steadily increased",
+    "5727c8863acd2414000dec49": "3 miles",
+    "5727c8863acd2414000dec4a": "twelve",
+    "5727c8863acd2414000dec4b": "Charles River",
+    "5727c8863acd2414000dec4c": "half a mile",
+    "5727cc15ff5b5019007d9576": "Allston",
+    "5727cc15ff5b5019007d9577": "John W. Weeks Bridge",
+    "5727cc15ff5b5019007d9578": "Longwood Medical and Academic Area",
+    "5727cd0f4b864d1900163d72": "fifty percent",
+    "5727cd0f4b864d1900163d73": "new and enlarged bridges, a shuttle service and/or a tram",
+    "5727cd0f4b864d1900163d74": "enhanced transit infrastructure, possible shuttles open to the public",
+    "5727cebc2ca10214002d96f0": "2,400",
+    "5727cebc2ca10214002d96f1": "7,200",
+    "5727cebc2ca10214002d96f2": "14,000",
+    "5727cebc2ca10214002d96f3": "1875",
+    "5727cebc2ca10214002d96f4": "1858",
+    "5727cff1ff5b5019007d95c4": "$32 billion",
+    "5727cff1ff5b5019007d95c5": "30%",
+    "5727cff1ff5b5019007d95c6": "Allston Science Complex",
+    "5727cff1ff5b5019007d95c7": "$4.093 million",
+    "5727cff1ff5b5019007d95c8": "$159 million",
+    "5727d0f73acd2414000ded13": "late 1980s",
+    "5727d0f73acd2414000ded14": "Duke Kent-Brown",
+    "5727d0f73acd2414000ded15": "$230 million",
+    "5727d1c93acd2414000ded3f": "5.3%",
+    "5727d1c93acd2414000ded40": "2007",
+    "5727d1c93acd2414000ded41": "the program was believed to disadvantage low-income and under-represented minority applicants applying to selective universities",
+    "5727d1c93acd2414000ded42": "2016",
+    "5727d3843acd2414000ded69": "seven",
+    "5727d3843acd2414000ded6a": "eight",
+    "5727d3843acd2414000ded6b": "reliance on teaching fellows",
+    "5727d4922ca10214002d977c": "mid-May",
+    "5727d4922ca10214002d977d": "four",
+    "5727d4922ca10214002d977e": "degrees summa cum laude",
+    "5727d4922ca10214002d977f": "60%",
+    "5727d6154b864d1900163e34": "$38,000",
+    "5727d6154b864d1900163e35": "$57,000",
+    "5727d6154b864d1900163e36": "10%",
+    "5727d6154b864d1900163e37": "$414 million",
+    "5727d6154b864d1900163e38": "88%",
+    "5727d6faff5b5019007d969a": "Widener Library",
+    "5727d6faff5b5019007d969c": "Cabot Science Library, Lamont Library, and Widener Library",
+    "5727d6faff5b5019007d969d": "Pusey Library",
+    "5727d6faff5b5019007d969b": "18 million",
+    "5727d88b4b864d1900163e5e": "three",
+    "5727d88b4b864d1900163e5f": "Western art",
+    "5727d88b4b864d1900163e60": "Peabody Museum of Archaeology and Ethnology",
+    "5727d9c43acd2414000dee17": "2003",
+    "5727d9c43acd2414000dee18": "2011",
+    "5727d9c43acd2414000dee19": "second",
+    "5727da564b864d1900163e8e": "42",
+    "5727da564b864d1900163e8f": "Yale University",
+    "5727da564b864d1900163e90": "every two years",
+    "5727db85ff5b5019007d96fc": "1875",
+    "5727db85ff5b5019007d96fd": "1903",
+    "5727db85ff5b5019007d96fe": "1906",
+    "5727db85ff5b5019007d96ff": "Yale",
+    "5727dc473acd2414000dee43": "Lavietes Pavilion",
+    "5727dc473acd2414000dee44": "Malkin Athletic Center",
+    "5727dc473acd2414000dee45": "three",
+    "5727dd2e4b864d1900163eba": "23",
+    "5727dd2e4b864d1900163ebb": "Thames River",
+    "5727dd2e4b864d1900163ebc": "Cornell",
+    "5727dd2e4b864d1900163ebd": "2003",
+    "5727de862ca10214002d9860": "Ban Ki-moon",
+    "5727de862ca10214002d9861": "Juan Manuel Santos",
+    "5727de862ca10214002d9862": "Jos\u00e9 Mar\u00eda Figueres",
+    "5727de862ca10214002d9863": "Benjamin Netanyahu",
+    "5727e0474b864d1900163f08": "Conan O'Brien",
+    "5727e0474b864d1900163f09": "Leonard Bernstein",
+    "5727e0474b864d1900163f0a": "Yo Yo Ma",
+    "5727e0474b864d1900163f0b": "W. E. B. Du Bois",
+    "5727e21e4b864d1900163f34": "Shing-Tung Yau",
+    "5727e21e4b864d1900163f35": "Alan Dershowitz and Lawrence Lessig",
+    "5727e21e4b864d1900163f36": "Stephen Greenblatt",
+    "5727c94bff5b5019007d954a": "Jacksonville",
+    "5727c94bff5b5019007d954b": "1,345,596",
+    "5727c94bff5b5019007d954c": "12th",
+    "5727c94bff5b5019007d954d": "Duval County",
+    "5727c94bff5b5019007d954e": "1968",
+    "5727cb4b2ca10214002d9676": "St. Johns River",
+    "5727cb4b2ca10214002d9677": "340 miles",
+    "5727cb4b2ca10214002d9678": "Fort Caroline",
+    "5727cb4b2ca10214002d9679": "Timucua",
+    "5727cb4b2ca10214002d967a": "Andrew Jackson",
+    "5727cd7dff5b5019007d958e": "third largest",
+    "5727cd7dff5b5019007d958f": "golf",
+    "5727cd7dff5b5019007d9590": "two",
+    "5727cd7dff5b5019007d9591": "Jacksonvillians",
+    "57280e1aff5b5019007d9bec": "thousands",
+    "57280e1aff5b5019007d9bed": "a University of North Florida team",
+    "57280e1aff5b5019007d9bee": "Mocama",
+    "57280e1aff5b5019007d9bef": "the historical era",
+    "57280e1aff5b5019007d9bf0": "Ossachite",
+    "57280fd3ff5b5019007d9c26": "Jean Ribault",
+    "57280fd3ff5b5019007d9c27": "France",
+    "57280fd3ff5b5019007d9c28": "Pedro Men\u00e9ndez de Avil\u00e9s",
+    "57280fd3ff5b5019007d9c29": "fort San Mateo",
+    "57280fd3ff5b5019007d9c2a": "Fort Caroline",
+    "572810ec2ca10214002d9d06": "French and Indian War",
+    "572810ec2ca10214002d9d07": "constructed the King's Road connecting St. Augustine to Georgia",
+    "572810ec2ca10214002d9d08": "cattle were brought across the river",
+    "572810ec2ca10214002d9d09": "Spain",
+    "572810ec2ca10214002d9d0a": "February 9, 1832",
+    "5728128cff5b5019007d9c76": "Confederate",
+    "5728128cff5b5019007d9c77": "the Brick Church",
+    "5728128cff5b5019007d9c78": "Battle of Olustee",
+    "5728128cff5b5019007d9c79": "Warfare and the long occupation",
+    "5728128cff5b5019007d9c7a": "1864",
+    "572815d9ff5b5019007d9cbc": "Reconstruction and the Gilded Age",
+    "572815d9ff5b5019007d9cbd": "Grover Cleveland",
+    "572815d9ff5b5019007d9cbe": "yellow fever outbreaks",
+    "572815d9ff5b5019007d9cbf": "extension of the Florida East Coast Railway",
+    "572815d9ff5b5019007d9cc0": "railroad",
+    "5728170d3acd2414000df443": "Spanish moss",
+    "5728170d3acd2414000df444": "2,000",
+    "5728170d3acd2414000df445": "declare martial law",
+    "5728170d3acd2414000df446": "Great Fire of 1901",
+    "57281940ff5b5019007d9d44": "New York\u2013based filmmakers",
+    "57281940ff5b5019007d9d45": "silent",
+    "57281940ff5b5019007d9d46": "Winter Film Capital of the World",
+    "57281940ff5b5019007d9d47": "Hollywood",
+    "57281ab63acd2414000df493": "construction of highways",
+    "57281ab63acd2414000df494": "55.1%",
+    "57281ab63acd2414000df495": "white flight",
+    "57281ab63acd2414000df496": "Mayor W. Haydon Burns",
+    "57281ab63acd2414000df497": "World War II",
+    "57281bb84b864d190016449a": "Much of the city's tax base dissipated",
+    "57281bb84b864d190016449b": "unincorporated suburbs",
+    "57281bb84b864d190016449c": "annexing outlying communities in order to create the needed tax base to improve services throughout the county",
+    "57281bb84b864d190016449d": "Voters outside the city limits",
+    "57281d494b864d19001644be": "through the traditional old boy network",
+    "57281d494b864d19001644bf": "11",
+    "57281d494b864d19001644c0": "Jacksonville Consolidation",
+    "57281d494b864d19001644c1": "Lower taxes",
+    "57281edd3acd2414000df4eb": "consolidation referendum was held in 1967, voters approved the plan",
+    "57281edd3acd2414000df4ec": "Hans Tanzler",
+    "57281edd3acd2414000df4ed": "the Consolidated City of Jacksonville",
+    "57281edd3acd2414000df4ee": "The Better Jacksonville Plan",
+    "57281edd3acd2414000df4ef": "half-penny sales tax",
+    "572820512ca10214002d9e72": "86.66%",
+    "572820512ca10214002d9e73": "St. Johns River",
+    "572820512ca10214002d9e74": "Trout River",
+    "572820512ca10214002d9e75": "13.34%",
+    "572820512ca10214002d9e76": "Baldwin",
+    "572821274b864d1900164510": "The tallest",
+    "572821274b864d1900164511": "Barnett Center",
+    "572821274b864d1900164512": "617 ft",
+    "572821274b864d1900164513": "28",
+    "572821274b864d1900164514": "flared base",
+    "5728223cff5b5019007d9dc4": "humid subtropical",
+    "5728223cff5b5019007d9dc5": "May through September",
+    "5728223cff5b5019007d9dc6": "mild and sunny",
+    "5728223cff5b5019007d9dc7": "low latitude",
+    "57282358ff5b5019007d9dde": "104 \u00b0F",
+    "57282358ff5b5019007d9ddf": "thunderstorms",
+    "57282358ff5b5019007d9de0": "extremely high humidity",
+    "57282358ff5b5019007d9de1": "January to 82 F in July",
+    "572824f13acd2414000df58f": "Hurricane Dora",
+    "572824f13acd2414000df590": "110 mph",
+    "572824f13acd2414000df591": "Beryl",
+    "572824f13acd2414000df592": "Saffir-Simpson",
+    "572824f13acd2414000df593": "2008",
+    "572826634b864d19001645be": "Arab",
+    "572826634b864d19001645bf": "821,784",
+    "572826634b864d19001645c0": "largest",
+    "572826634b864d19001645c1": "Filipino",
+    "572827fc3acd2414000df5bb": "29.7%",
+    "572827fc3acd2414000df5bc": "23.9%",
+    "572827fc3acd2414000df5bd": "married couples",
+    "572827fc3acd2414000df5be": "91.3",
+    "5727e6cbff5b5019007d97ee": "40%",
+    "5727e6cbff5b5019007d97ef": "3.5 billion",
+    "5727e6cbff5b5019007d97f0": "$759,900",
+    "5727e6cbff5b5019007d97f1": "by using net wealth",
+    "5727e6cbff5b5019007d97f2": "by using net wealth",
+    "5729d36b1d04691400779607": "40%",
+    "5729d36b1d04691400779608": "financial assets",
+    "5729d36b1d04691400779609": "$41 trillion",
+    "5729d36b1d0469140077960a": "more than half",
+    "5729d36b1d0469140077960b": "greater tendency to take on debts",
+    "5727e8424b864d1900163fc0": "top 400",
+    "5727e8424b864d1900163fc1": "New York Times",
+    "5727e8424b864d1900163fc2": "Inherited wealth may help explain why many Americans who have become rich may have had a \"substantial head start",
+    "5727e8424b864d1900163fc3": "Inherited wealth",
+    "5729d44b1d04691400779611": "more wealth",
+    "5729d44b1d04691400779612": "richest 1 percent",
+    "5729d44b1d04691400779613": "Inherited wealth",
+    "5729d44b1d04691400779614": "over 60 percent",
+    "5729d44b1d04691400779615": "Institute for Policy Studies",
+    "5727e9523acd2414000def95": "Neoclassical economics",
+    "5727e9523acd2414000def96": "productivity gap between highly-paid professions and lower-paid professions",
+    "5727e9523acd2414000def97": "differences in value added by different classifications of workers",
+    "5727e9523acd2414000def98": "inequality",
+    "5727e9523acd2414000def99": "marginal value added of each economic actor",
+    "5729d51d3f37b3190047858f": "differences in value added by labor, capital and land",
+    "5729d51d3f37b31900478590": "value added by different classifications of workers",
+    "5729d51d3f37b31900478591": "wages and profits",
+    "5729d51d3f37b31900478592": "worker, capitalist/business owner, landlord",
+    "5729d51d3f37b31900478593": "productivity gap between highly-paid professions and lower-paid professions",
+    "5727ea45ff5b5019007d986e": "competitive pressure to reduce costs and maximize profits",
+    "5727ea45ff5b5019007d986f": "downward pressure on wages",
+    "5727ea45ff5b5019007d9870": "increasing unemployment",
+    "5727ea45ff5b5019007d9871": "downward pressure on wages",
+    "5729d609af94a219006aa661": "labor inputs (workers)",
+    "5729d609af94a219006aa662": "to reduce costs and maximize profits",
+    "5729d609af94a219006aa663": "Marxian analysis",
+    "5729d609af94a219006aa664": "productivity",
+    "5729d609af94a219006aa665": "relatively stagnant",
+    "5727ec062ca10214002d99b6": "workers wages",
+    "5727ec062ca10214002d99b7": "law of supply and demand",
+    "5727ec062ca10214002d99b8": "Their competitors will take advantage of the situation by offering a higher wage the best of their labor",
+    "5727ec062ca10214002d99b9": "by offering a higher wage the best of their labor",
+    "5727ec062ca10214002d99ba": "unfair",
+    "5729d878af94a219006aa66b": "the market",
+    "5729d878af94a219006aa66c": "prices",
+    "5729d878af94a219006aa66d": "wages",
+    "5729d878af94a219006aa66e": "markets",
+    "5729d878af94a219006aa66f": "high levels of inequality",
+    "5727ed2e3acd2414000df001": "high demand",
+    "5727ed2e3acd2414000df002": "low demand",
+    "5727ed2e3acd2414000df003": "high demand",
+    "5727ed2e3acd2414000df004": "collective bargaining, political influence, or corruption",
+    "5727ed2e3acd2414000df005": "Professional and labor organizations",
+    "5729da0faf94a219006aa675": "low wage",
+    "5729da0faf94a219006aa676": "competition",
+    "5729da0faf94a219006aa677": "the expendable nature of the worker in relation to his or her particular job",
+    "5729da0faf94a219006aa678": "low wage",
+    "5729da0faf94a219006aa679": "employers for employees",
+    "5727ee372ca10214002d99ec": "entrepreneurship rates",
+    "5727ee372ca10214002d99ed": "self-employment",
+    "5727ee372ca10214002d99ee": "Necessity-based entrepreneurship",
+    "5727ee372ca10214002d99ef": "opportunity-based entrepreneurship",
+    "5727ee372ca10214002d99f0": "opportunity-based",
+    "5729de24af94a219006aa689": "higher economic inequality",
+    "5729de24af94a219006aa68a": "higher economic inequality",
+    "5729de24af94a219006aa68b": "Necessity-based",
+    "5729de24af94a219006aa68c": "achievement-oriented motivations",
+    "5729de24af94a219006aa68d": "positive",
+    "5727ef664b864d1900164060": "progressive tax",
+    "5727ef664b864d1900164061": "top tax rate",
+    "5727ef664b864d1900164062": "steeper tax progressivity",
+    "5727ef664b864d1900164063": "progressive tax system",
+    "5729e02f1d04691400779639": "tax rate",
+    "5729e02f1d0469140077963a": "the level of the top tax rate",
+    "5729e02f1d0469140077963b": "steeper tax progressivity applied to social spending",
+    "5729e02f1d0469140077963c": "Gini index",
+    "5727f05b4b864d1900164068": "Education",
+    "5727f05b4b864d1900164069": "education",
+    "5727f05b4b864d190016406a": "lower incomes",
+    "5727f05b4b864d190016406b": "the poor",
+    "5727f05b4b864d190016406c": "lower aggregate savings and investment",
+    "5729e1101d04691400779641": "variation in individuals' access to education",
+    "5729e1101d04691400779642": "high wages",
+    "5729e1101d04691400779643": "lower",
+    "5729e1101d04691400779644": "lower incomes",
+    "5729e1101d04691400779645": "education",
+    "5727f16c3acd2414000df05b": "increasing access to education",
+    "5727f16c3acd2414000df05c": "$105 billion",
+    "5727f16c3acd2414000df05d": "boom-and-bust cycles",
+    "5727f16c3acd2414000df05e": "economists with the Standard & Poor's rating agency",
+    "5729e1e36aef0514001550ba": "2014",
+    "5729e1e36aef0514001550bb": "made it more prone to boom-and-bust cycles",
+    "5729e1e36aef0514001550bc": "increasing access to education",
+    "5729e1e36aef0514001550bd": "$105 billion",
+    "5729e1e36aef0514001550be": "boom-and-bust cycles",
+    "5727f2714b864d1900164072": "1910\u20131940",
+    "5727f2714b864d1900164073": "decrease in the price of skilled labor",
+    "5727f2714b864d1900164074": "decrease in the price of skilled labor",
+    "5727f2714b864d1900164075": "low economic growth",
+    "5727f2714b864d1900164076": "decrease in wages",
+    "5729e2b76aef0514001550ce": "1910\u20131940",
+    "5729e2b76aef0514001550cf": "decrease in the price of skilled labor",
+    "5729e2b76aef0514001550d0": "it differs from the present high school education, which is regarded as a stepping-stone to acquire college and advanced degrees",
+    "5729e2b76aef0514001550d1": "Education",
+    "5729e2b76aef0514001550d2": "gender inequality in education",
+    "5727f6723acd2414000df0e7": "union",
+    "5727f6723acd2414000df0e8": "continental European countries",
+    "5727f6723acd2414000df0ea": "little",
+    "5727f6723acd2414000df0e9": "Anglo-American liberal policies in comparison to continental European liberalism",
+    "5729e38daf94a219006aa69d": "economic inequality",
+    "5729e38daf94a219006aa69e": "social exclusion",
+    "5729e38daf94a219006aa69f": "CEPR",
+    "5729e38daf94a219006aa6a0": "little",
+    "5729e38daf94a219006aa6a1": "lower",
+    "5727f7523acd2414000df10d": "Scandinavia",
+    "5727f7523acd2414000df10e": "high inequality",
+    "5727f7523acd2414000df10f": "the decline of organized labor",
+    "5727f7523acd2414000df110": "the decline of organized labor",
+    "5729e4291d04691400779651": "Sociologist",
+    "5729e4291d04691400779652": "University of Washington",
+    "5729e4291d04691400779653": "the decline of organized labor",
+    "5729e4291d04691400779654": "high",
+    "5729e4291d04691400779655": "weak labor movements",
+    "5727fc37ff5b5019007d9a18": "reduced wages",
+    "5727fc37ff5b5019007d9a19": "increased trade with poor countries and the fragmentation of the means of production, resulting in low skilled jobs becoming more tradeable",
+    "5727fc37ff5b5019007d9a1a": "technological innovation",
+    "5727fc37ff5b5019007d9a1b": "machine labor",
+    "5729f1283f37b319004785d7": "global to a domestic",
+    "5729f1283f37b319004785d8": "low-skilled workers",
+    "5729f1283f37b319004785d9": "trade liberalisation",
+    "5729f1283f37b319004785da": "minor",
+    "5729f1283f37b319004785db": "machine labor",
+    "5727fd123acd2414000df185": "53%",
+    "5727fd123acd2414000df186": "40%",
+    "5727fd123acd2414000df187": "there is still a difference in earnings between women and men",
+    "5727fd123acd2414000df188": "males in the labor market",
+    "5729f1c13f37b319004785e7": "Gender",
+    "5729f1c13f37b319004785e8": "males in the labor market",
+    "5729f1c13f37b319004785e9": "women",
+    "5729f1c13f37b319004785ea": "Thomas Sowell",
+    "5729f1c13f37b319004785eb": "a difference",
+    "5727ff083acd2414000df1ab": "social welfare programs",
+    "5727ff083acd2414000df1ac": "lower levels",
+    "5727ff083acd2414000df1ad": "it acquires more capital",
+    "5727ff083acd2414000df1ae": "social welfare programs",
+    "5729f24baf94a219006aa6dd": "Economist",
+    "5729f24baf94a219006aa6de": "levels of economic inequality",
+    "5729f24baf94a219006aa6df": "more capital",
+    "5729f24baf94a219006aa6e0": "more wealth and income",
+    "5729f24baf94a219006aa6e1": "lower levels of inequality",
+    "57287b322ca10214002da3be": "1910 to 1940",
+    "57287b322ca10214002da3bf": "1970s",
+    "57287b322ca10214002da3c0": "manufacturing",
+    "57287b322ca10214002da3c1": "manufacturing",
+    "5729f3883f37b319004785f1": "Kuznets",
+    "5729f3883f37b319004785f2": "Kuznets curve",
+    "5729f3883f37b319004785f3": "very weak",
+    "5729f3883f37b319004785f4": "income inequality will eventually decrease",
+    "5729f3883f37b319004785f5": "manufacturing sector to the service sector",
+    "57287c2bff5b5019007da26c": "Wealth concentration",
+    "57287c2bff5b5019007da26d": "those who already hold wealth have the means to invest in new sources of creating wealth or to otherwise leverage the accumulation of wealth",
+    "57287c2bff5b5019007da26e": "wealth condensation",
+    "57287c2bff5b5019007da26f": "wealth condensation",
+    "5729f4273f37b319004785fb": "in the possession of already-wealthy individuals or entities",
+    "5729f4273f37b319004785fc": "those who already hold wealth have the means to invest in new sources of creating wealth",
+    "5729f4273f37b319004785fd": "wealth condensation",
+    "5729f4273f37b319004785fe": "Thomas Piketty",
+    "5729f4273f37b319004785ff": "higher returns",
+    "5729f4e46aef051400155157": "market forces",
+    "5729f4e46aef051400155156": "Economist",
+    "5729f4e46aef051400155158": "rare and desired skills",
+    "5729f4e46aef051400155159": "political power",
+    "5729f4e46aef05140015515a": "rent-seeking",
+    "5729f60caf94a219006aa6ef": "inequality",
+    "5729f60caf94a219006aa6f0": "human capital",
+    "5729f60caf94a219006aa6f1": "life expectancy",
+    "5729f60caf94a219006aa6f2": "inequality",
+    "5729f60caf94a219006aa6f3": "lower",
+    "572a05eb3f37b31900478653": "2013",
+    "572a05eb3f37b31900478654": "rising inequality",
+    "572a05eb3f37b31900478655": "negative",
+    "572a05eb3f37b31900478656": "social dislocation, unrest and conflict",
+    "572a05eb3f37b31900478657": "economic",
+    "572a06866aef0514001551be": "British",
+    "572a06866aef0514001551bf": "higher",
+    "572a06866aef0514001551c0": "lower",
+    "572a06866aef0514001551c2": "23",
+    "572a06866aef0514001551c1": "equality",
+    "572a070c6aef0514001551c8": "better health and longer lives",
+    "572a070c6aef0514001551c9": "among poorer countries",
+    "572a070c6aef0514001551ca": "life expectancy",
+    "572a070c6aef0514001551cb": "Americans",
+    "572a070c6aef0514001551cc": "80 years) and Japan (82) \u2013 where income was more equally distributed",
+    "572a07a86aef0514001551d2": "income inequality",
+    "572a07a86aef0514001551d3": "Richard Wilkinson and Kate Pickett",
+    "572a07a86aef0514001551d4": "nine",
+    "572a07a86aef0514001551d5": "countries with bigger income inequalities",
+    "572a07a86aef0514001551d6": "greater equality but not per capita income",
+    "572a0a391d046914007796df": "inequality",
+    "572a0a391d046914007796e0": "homicides",
+    "572a0a391d046914007796e1": "fifty",
+    "572a0a391d046914007796e3": "differences in the amount of inequality in each province or state",
+    "572a0a391d046914007796e2": "tenfold",
+    "572a0bafaf94a219006aa765": "good",
+    "572a0bafaf94a219006aa766": "distributive efficiency",
+    "572a0bafaf94a219006aa767": "a great deal of utility",
+    "572a0bafaf94a219006aa768": "decreases",
+    "572a0bafaf94a219006aa769": "higher aggregate utility",
+    "572a0c541d046914007796f3": "consumption",
+    "572a0c541d046914007796f4": "Conservative",
+    "572a0c541d046914007796f5": "2001",
+    "572a0c541d046914007796f6": "Thomas B. Edsall",
+    "572a0c541d046914007796f7": "journalist",
+    "572a0d21af94a219006aa783": "Central Banking economist",
+    "572a0d21af94a219006aa784": "systematic economic inequalities",
+    "572a0d21af94a219006aa785": "Financial crisis of 2007\u201308",
+    "572a0d21af94a219006aa786": "easier credit",
+    "572a0d21af94a219006aa787": "unsustainable monetary stimulation",
+    "572a0e0e1d04691400779707": "inequality in wealth and income",
+    "572a0e0e1d04691400779708": "the quality of a country's institutions and high levels of education",
+    "572a0e0e1d04691400779709": "declines",
+    "572a0e0e1d0469140077970a": "higher GDP growth",
+    "572a0e0e1d0469140077970b": "The poor and the middle class",
+    "572a0ecb1d04691400779717": "economists",
+    "572a0ecb1d04691400779718": "economic growth",
+    "572a0ecb1d04691400779719": "subsequent long-run economic growth",
+    "572a0ecb1d0469140077971a": "it is a waste of resources",
+    "572a0ecb1d0469140077971b": "inequality-associated effects",
+    "572a1046af94a219006aa78d": "evidence",
+    "572a1046af94a219006aa78e": "limiting aggregate demand",
+    "572a1046af94a219006aa78f": "Economist",
+    "572a1046af94a219006aa790": "increasing importance of human capital in development",
+    "572a1046af94a219006aa791": "widespread education",
+    "572a11663f37b31900478693": "1993",
+    "572a11663f37b31900478694": "detrimental",
+    "572a11663f37b31900478695": "the channels through which inequality may affect economic growth",
+    "572a11663f37b31900478696": "redistributive taxation",
+    "572a11663f37b31900478697": "politically and socially unstable",
+    "572a12381d0469140077972d": "reduce",
+    "572a12381d0469140077972e": "encourage",
+    "572a12381d0469140077972c": "growth and investment",
+    "572a12381d0469140077972b": "Harvard",
+    "572a12381d0469140077972f": "between 1960 and 2000",
+    "572a13841d0469140077973b": "Kuznets curve hypothesis",
+    "572a13841d0469140077973c": "the 1970s",
+    "572a13841d0469140077973d": "Economist Thomas Piketty",
+    "572a13841d0469140077973e": "Economist",
+    "572a13841d0469140077973f": "wars",
+    "572a18a4af94a219006aa7df": "1970s",
+    "572a18a4af94a219006aa7e0": "reduced consumer demand",
+    "572a18a4af94a219006aa7e1": "risen with increased income inequality",
+    "572a18a4af94a219006aa7e2": "several years",
+    "572a18a4af94a219006aa7e3": "more equality in the income distribution",
+    "572a1a5c6aef051400155284": "special efforts",
+    "572a1a5c6aef051400155285": "the existing level of inequality",
+    "572a1a5c6aef051400155286": "the same reduction",
+    "572a1a5c6aef051400155287": "United Nations",
+    "572a1a5c6aef051400155288": "reducing poverty",
+    "572a1ba46aef05140015528e": "land and housing",
+    "572a1ba46aef05140015528f": "through various associations and other arrangements",
+    "572a1ba46aef051400155290": "extra-legal",
+    "572a1ba46aef051400155291": "200",
+    "572a1ba46aef051400155292": "government land",
+    "572a1c943f37b319004786e1": "affordable housing",
+    "572a1c943f37b319004786e2": "quality rental units",
+    "572a1c943f37b319004786e3": "the demand for higher quality housing increased",
+    "572a1c943f37b319004786e4": "gentrification of older neighbourhoods",
+    "572a1c943f37b319004786e5": "ad valorem property tax policy",
+    "572a1dbb3f37b319004786f5": "everyone",
+    "572a1dbb3f37b319004786f6": "their finances",
+    "572a1dbb3f37b319004786f7": "aspirational consumption",
+    "572a1dbb3f37b319004786f8": "taking on debt",
+    "572a1dbb3f37b319004786f9": "economic instability",
+    "572a1f086aef0514001552c0": "created",
+    "572a1f086aef0514001552c1": "environmental degradation",
+    "572a1f086aef0514001552c2": "environmental degradation",
+    "572a1f086aef0514001552c3": "If (as WWF argued), population levels would start to drop to a sustainable level",
+    "572a1fe16aef0514001552d0": "private ownership of the means of production by a class of owners",
+    "572a1fe16aef0514001552d1": "a small portion of the population lives off unearned property income",
+    "572a1fe16aef0514001552d2": "wage or salary",
+    "572a1fe16aef0514001552d3": "socially owned",
+    "572a1fe16aef0514001552d4": "reflective",
+    "572a20816aef0514001552e4": "Robert Nozick",
+    "572a20816aef0514001552e5": "taxation",
+    "572a20816aef0514001552e6": "force",
+    "572a20816aef0514001552e7": "forceful taking of property",
+    "572a20816aef0514001552e8": "when they improve society as a whole",
+    "572a213e6aef0514001552ee": "capability deprivation",
+    "572a213e6aef0514001552ef": "the end itself",
+    "572a213e6aef0514001552f0": "to \u201cwid[en] people\u2019s choices and the level of their achieved well-being",
+    "572a213e6aef0514001552f1": "to \u201cwid[en] people\u2019s choices and the level of their achieved well-being",
+    "572a213e6aef0514001552f2": "the ability to pursue valued goals",
+    "572a2224af94a219006aa823": "they are in some way deprived of earning as much income as they would otherwise",
+    "572a2224af94a219006aa824": "earn as much as a healthy young man",
+    "572a2224af94a219006aa825": "gender roles and customs",
+    "572a2224af94a219006aa826": "fear of their lives",
+    "572a2224af94a219006aa827": "a better relevant income",
+    "5727efabff5b5019007d98e2": "the BBC",
+    "5727efabff5b5019007d98e3": "1963",
+    "5727efabff5b5019007d98e4": "TARDIS",
+    "5727efabff5b5019007d98e5": "a blue British police box",
+    "5727efabff5b5019007d98e6": "science-fiction",
+    "5727f0b1ff5b5019007d98f2": "1963 to 1989",
+    "5727f0b1ff5b5019007d98f3": "Russell T Davies",
+    "5727f0b1ff5b5019007d98f4": "K-9 and Company",
+    "5727f0b1ff5b5019007d98f5": "BBC Wales",
+    "5727f0b1ff5b5019007d98f6": "Christopher Eccleston",
+    "5727f18c3acd2414000df063": "Twelve",
+    "5727f18c3acd2414000df064": "Peter Capaldi",
+    "5727f18c3acd2414000df065": "The Time of the Doctor",
+    "5727f18c3acd2414000df066": "this is a life process of Time Lords",
+    "5727f18c3acd2414000df067": "new personality",
+    "5727f2583acd2414000df087": "Gallifrey",
+    "5727f2583acd2414000df088": "Mark I Type 40 TARDIS",
+    "5727f2583acd2414000df089": "chameleon circuit",
+    "5727f2583acd2414000df08a": "chameleon circuit",
+    "5727f2583acd2414000df08b": "malfunction in the chameleon circuit",
+    "5727f3193acd2414000df0a5": "rarely",
+    "5727f3193acd2414000df0a6": "the Master",
+    "5727f3193acd2414000df0a7": "regenerate",
+    "5727f3193acd2414000df0a8": "humans",
+    "5727f3193acd2414000df0a9": "Time Lord",
+    "5727f44c2ca10214002d9a32": "23 November 1963",
+    "5727f44c2ca10214002d9a33": "aliens",
+    "5727f44c2ca10214002d9a34": "the programme was not permitted to contain any \"bug-eyed monsters",
+    "5727f44c2ca10214002d9a35": "Terry Nation",
+    "5727f44c2ca10214002d9a36": "25 minutes",
+    "5727f5622ca10214002d9a44": "26",
+    "5727f5622ca10214002d9a45": "Jonathan Powell",
+    "5727f5622ca10214002d9a46": "Doctor Who: More Than 30 Years in the TARDIS",
+    "5727f5622ca10214002d9a47": "the series would return.",
+    "5727f5622ca10214002d9a48": "BBC 1",
+    "5727f678ff5b5019007d9956": "relaunch the show",
+    "5727f678ff5b5019007d9957": "Philip Segal",
+    "5727f678ff5b5019007d9958": "Fox",
+    "5727f678ff5b5019007d9959": "9.1 million",
+    "5727f678ff5b5019007d995a": "United States",
+    "5727f746ff5b5019007d9960": "Rose",
+    "5727f746ff5b5019007d9961": "2005",
+    "5727f746ff5b5019007d9963": "2009",
+    "5727f746ff5b5019007d9964": "Chris Chibnall",
+    "5727f746ff5b5019007d9962": "Christmas Day specials",
+    "5727f8342ca10214002d9a7c": "1963\u20131989",
+    "5727f8342ca10214002d9a7d": "The 2005 version of Doctor Who",
+    "5727f8342ca10214002d9a7e": "2005",
+    "5727f8342ca10214002d9a7f": "Battlestar Galactica and Bionic Woman",
+    "5727f8342ca10214002d9a80": "Battlestar Galactica and Bionic Woman",
+    "5727fc7f4b864d1900164160": "30 November 1963",
+    "5727fc7f4b864d1900164161": "ten minutes",
+    "5727fc7f4b864d1900164162": "ten minutes",
+    "5727fc7f4b864d1900164163": "assassination of US President John F. Kennedy",
+    "5727fc7f4b864d1900164164": "power blackouts",
+    "5727fdb94b864d190016417c": "Hiding behind (or 'watching from behind') the sofa",
+    "5727fdb94b864d190016417d": "the Museum of the Moving Image in London",
+    "5727fdb94b864d190016417e": "Behind the Sofa\"",
+    "5727fdb94b864d190016417f": "scariest TV show of all time",
+    "5727fdb94b864d1900164180": "Digital Spy",
+    "5727fed73acd2414000df1a1": "Doctor Who",
+    "5727fed73acd2414000df1a2": "3%",
+    "5727fed73acd2414000df1a3": "Philip Howard",
+    "5727fed73acd2414000df1a4": "Monopoly",
+    "5727fed73acd2414000df1a5": "The Times",
+    "5727ffb5ff5b5019007d9a8c": "TARDIS",
+    "5727ffb5ff5b5019007d9a8d": "a police box",
+    "5727ffb5ff5b5019007d9a8e": "police box as a time machine",
+    "5727ffb5ff5b5019007d9a8f": "the Metropolitan Police Authority",
+    "5727ffb5ff5b5019007d9a90": "2002",
+    "572800b03acd2414000df1f1": "26",
+    "572800b03acd2414000df1f2": "6 December 1989",
+    "572800b03acd2414000df1f3": "12",
+    "572800b03acd2414000df1f4": "The Master",
+    "572800b03acd2414000df1f5": "Black Guardian Trilogy",
+    "572801823acd2414000df1fb": "2005",
+    "572801823acd2414000df1fc": "60 minutes",
+    "572801823acd2414000df1fd": "Christmas Day",
+    "572801823acd2414000df1fe": "Journey's End",
+    "572801823acd2414000df1ff": "2010",
+    "5728023a4b864d19001641f4": "826",
+    "5728023a4b864d19001641f5": "25-minute episodes",
+    "5728023a4b864d19001641f6": "eight",
+    "5728023a4b864d19001641f7": "60 minutes",
+    "5728023a4b864d19001641f8": "2009",
+    "572803493acd2414000df229": "William Hartnell and Patrick Troughton",
+    "572803493acd2414000df22a": "97",
+    "572803493acd2414000df22b": "3, 4, & 5",
+    "572803493acd2414000df22c": "1978",
+    "572803493acd2414000df22d": "1964 and 1973",
+    "572804522ca10214002d9b94": "bought prints for broadcast",
+    "572804522ca10214002d9b95": "fans",
+    "572804522ca10214002d9b97": "Mission to the Unknown",
+    "572804522ca10214002d9b96": "8 mm cine film",
+    "572804522ca10214002d9b98": "home viewers",
+    "572805363acd2414000df26d": "the BBC",
+    "572805363acd2414000df26e": "Cosgrove Hall",
+    "572805363acd2414000df26f": "1968",
+    "572805363acd2414000df270": "Theta-Sigma",
+    "572805363acd2414000df271": "November 2006",
+    "57280757ff5b5019007d9b42": "renewal",
+    "57280757ff5b5019007d9b43": "the Doctor's third on-screen regeneration",
+    "57280757ff5b5019007d9b44": "William Hartnell's poor health",
+    "57280757ff5b5019007d9b45": "renewal",
+    "57280757ff5b5019007d9b46": "change of appearance",
+    "572808bf4b864d190016429a": "12",
+    "572808bf4b864d190016429b": "13",
+    "572808bf4b864d190016429c": "The Time of the Doctor",
+    "572808bf4b864d190016429d": "The Deadly Assassin and Mawdryn Undead",
+    "572808bf4b864d190016429e": "1996",
+    "572809ab4b864d19001642ae": "John Hurt",
+    "572809ab4b864d19001642af": "The Day of the Doctor",
+    "572809ab4b864d19001642b0": "Michael Jayston",
+    "572809ab4b864d19001642b1": "The Trial of a Time Lord",
+    "572809ab4b864d19001642b2": "McGann and Eccleston",
+    "57280b2b2ca10214002d9c66": "the War Doctor",
+    "57280b2b2ca10214002d9c67": "The Three Doctors",
+    "57280b2b2ca10214002d9c68": "Peter Davison",
+    "57280b2b2ca10214002d9c6a": "The Space Museum",
+    "57280b2b2ca10214002d9c69": "The Day of the Doctor",
+    "57280cac2ca10214002d9ca8": "Peter Davison, Colin Baker and Sylvester McCoy",
+    "57280cac2ca10214002d9ca9": "Zagreus",
+    "57280cac2ca10214002d9caa": "Peter Davison, Colin Baker, Sylvester McCoy and Paul McGann",
+    "57280cac2ca10214002d9cab": "Colin Baker and Sylvester McCoy",
+    "57280cac2ca10214002d9cac": "2003",
+    "57280e323acd2414000df349": "The Time of the Doctor",
+    "57280e323acd2414000df34a": "The Brain of Morbius",
+    "57280e323acd2414000df34b": "Mawdryn Undead",
+    "57280e323acd2414000df34c": "The Lodger",
+    "57280e323acd2414000df34d": "1983",
+    "57280f0d3acd2414000df35b": "An Unearthly Child",
+    "57280f0d3acd2414000df35c": "Susan Foreman",
+    "57280f0d3acd2414000df35d": "2005",
+    "57280f0d3acd2414000df35e": "destroyed",
+    "57280f0d3acd2414000df35f": "Smith and Jones",
+    "5728103eff5b5019007d9c30": "human",
+    "5728103eff5b5019007d9c31": "The Deadly Assassin",
+    "5728103eff5b5019007d9c33": "Romana",
+    "5728103eff5b5019007d9c34": "teachers",
+    "5728103eff5b5019007d9c32": "Romana",
+    "572811434b864d190016438c": "female",
+    "572811434b864d190016438d": "Rose Tyler (Billie Piper), Martha Jones (Freema Agyeman), and Donna Noble",
+    "572811434b864d190016438e": "Amy Pond",
+    "572811434b864d190016438f": "Pearl Mackie",
+    "572811434b864d1900164390": "Catherine Tate",
+    "572812142ca10214002d9d2a": "Russell T Davies",
+    "572812142ca10214002d9d2b": "series 1",
+    "572812142ca10214002d9d2c": "Cybermen",
+    "572812142ca10214002d9d2d": "series 3",
+    "572812142ca10214002d9d2e": "Zygons",
+    "572812eaff5b5019007d9c80": "The Dalek race",
+    "572812eaff5b5019007d9c81": "Skaro",
+    "572812eaff5b5019007d9c82": "exterminate\" all non-Dalek beings",
+    "572812eaff5b5019007d9c83": "Davros",
+    "572812eaff5b5019007d9c84": "their eyestalk",
+    "572813b52ca10214002d9d68": "The Master",
+    "572813b52ca10214002d9d69": "Professor Moriarty to the Doctor's Sherlock Holmes",
+    "572813b52ca10214002d9d6a": "Eric Roberts",
+    "572813b52ca10214002d9d6b": "Professor Moriarty to the Doctor's Sherlock Holmes",
+    "572813b52ca10214002d9d6c": "Roger Delgado",
+    "572814882ca10214002d9d72": "Michelle Gomez",
+    "572814882ca10214002d9d73": "Utopia",
+    "572814882ca10214002d9d74": "2014",
+    "572814882ca10214002d9d75": "Missy",
+    "572814882ca10214002d9d76": "Michelle Gomez",
+    "572816213acd2414000df429": "Ron Grainer",
+    "572816213acd2414000df42a": "BBC Radiophonic Workshop",
+    "572816213acd2414000df42b": "musique concr\u00e8te",
+    "572816213acd2414000df42c": "1979\u201380",
+    "572816213acd2414000df42d": "\"Did I write that?\"",
+    "5728177f2ca10214002d9db0": "Peter Howell",
+    "5728177f2ca10214002d9db1": "John Debney",
+    "5728177f2ca10214002d9db2": "Seventh Doctor",
+    "5728177f2ca10214002d9db3": "Murray Gold",
+    "572817802ca10214002d9db4": "The Christmas Invasion",
+    "5728185f3acd2414000df45b": "Voyage of the Damned",
+    "5728185f3acd2414000df45d": "Classic FM's Hall of Fame",
+    "5728185f3acd2414000df45c": "2010",
+    "5728185f3acd2414000df45e": "228",
+    "5728185f3acd2414000df45f": "Gold",
+    "572819864b864d190016447e": "Jon Pertwee",
+    "572819864b864d190016447f": "Mankind",
+    "572819864b864d1900164480": "24",
+    "572819864b864d1900164481": "The Timelords",
+    "572819864b864d1900164482": "Doctorin' the Tardis\" under the name The Timelords",
+    "57281a952ca10214002d9dea": "Dudley Simpson",
+    "57281a952ca10214002d9deb": "Planet of Giants",
+    "57281a952ca10214002d9dec": "15 years",
+    "57281a952ca10214002d9ded": "Planet of Giants",
+    "57281a952ca10214002d9dee": "The Talons of Weng-Chiang",
+    "57281bbc2ca10214002d9dfa": "BBC National Orchestra of Wales",
+    "57281bbc2ca10214002d9dfb": "David Tennant",
+    "57281bbc2ca10214002d9dfc": "27 July 2008",
+    "57281bbc2ca10214002d9dfd": "Music of the Spheres",
+    "57281bbc2ca10214002d9dfe": "Murray Gold and Ben Foster",
+    "57281cb22ca10214002d9e1e": "Six",
+    "57281cb22ca10214002d9e1f": "the second and third featured music from the third and fourth series",
+    "57281cb22ca10214002d9e20": "music from the 2008\u20132010 specials",
+    "57281cb22ca10214002d9e21": "The Next Doctor to End of Time Part 2",
+    "57281cb22ca10214002d9e22": "8 November 2010",
+    "57281f203acd2414000df4f5": "The original logo",
+    "57281f203acd2414000df4f6": "the Twelfth Doctor",
+    "57281f203acd2414000df4f7": "current Doctor Who logo",
+    "57281f203acd2414000df4f8": "The logo from 1973\u201380",
+    "57281f203acd2414000df4f9": "the First Doctor",
+    "57282036ff5b5019007d9d9c": "the assassination of John F. Kennedy",
+    "57282036ff5b5019007d9d9d": "BBC One channel",
+    "57282036ff5b5019007d9d9e": "1970s",
+    "57282036ff5b5019007d9d9f": "circa 1964\u20131965",
+    "57282036ff5b5019007d9da0": "BBC One",
+    "572821ceff5b5019007d9db2": "2005",
+    "572821ceff5b5019007d9db3": "late 1980s performance of three to five million viewers",
+    "572821ceff5b5019007d9db4": "Coronation Street",
+    "572821ceff5b5019007d9db5": "three to five million",
+    "572821ceff5b5019007d9db6": "2005",
+    "5728231fff5b5019007d9dd4": "PBS",
+    "5728231fff5b5019007d9dd5": "New Zealand",
+    "5728231fff5b5019007d9dd6": "Edmonton, Canada",
+    "5728231fff5b5019007d9dd7": "15",
+    "5728231fff5b5019007d9dd8": "23 November",
+    "5728245b2ca10214002d9ed6": "ABC",
+    "5728245b2ca10214002d9ed7": "partial funding",
+    "5728245b2ca10214002d9ed8": "SyFy",
+    "5728245b2ca10214002d9ed9": "weekly screenings of all available classic episodes",
+    "5728245b2ca10214002d9eda": "ABC",
+    "572825714b864d1900164590": "1976",
+    "572825714b864d1900164591": "The Three Doctors",
+    "572825714b864d1900164592": "CBC",
+    "572825714b864d1900164593": "The Talons of Weng-Chiang",
+    "572825714b864d1900164594": "Judith Merril",
+    "572826762ca10214002d9f2c": "Christopher Eccleston",
+    "572826762ca10214002d9f2d": "excerpts from the Doctor Who Confidential documentary",
+    "572826762ca10214002d9f2e": "The Christmas Invasion",
+    "572826762ca10214002d9f2f": "9 October 2006",
+    "572826762ca10214002d9f30": "Thanksgiving",
+    "5728274cff5b5019007d9e26": "United Kingdom, Australia, Canada and the United States",
+    "5728274cff5b5019007d9e27": "Eight original series serials",
+    "5728274cff5b5019007d9e28": "The Infinite Quest",
+    "5728274cff5b5019007d9e29": "Spearhead from Space",
+    "5728274cff5b5019007d9e2a": "2009 onwards",
+    "572828383acd2414000df5c3": "Trevor Martin",
+    "572828383acd2414000df5c4": "Doctor Who \u2013 The Ultimate Adventure",
+    "572828383acd2414000df5c5": "The Curse of the Daleks",
+    "572828383acd2414000df5c6": "Doctor Who \u2013 The Ultimate Adventure",
+    "572828383acd2414000df5c7": "David Banks",
+    "572829532ca10214002d9fa2": "Torchwood",
+    "572829532ca10214002d9fa3": "22 October 2006",
+    "572829532ca10214002d9fa4": "2008",
+    "572829532ca10214002d9fa5": "Children of Earth",
+    "572829532ca10214002d9fa6": "Torchwood: Miracle Day",
+    "57282f204b864d190016468a": "Elisabeth Sladen",
+    "57282f204b864d190016468b": "New Year's Day 2007",
+    "57282f204b864d190016468c": "2009",
+    "57282f204b864d190016468d": "2010",
+    "57282f204b864d190016468e": "the death of Elisabeth Sladen",
+    "5728303e4b864d19001646aa": "Dimensions in Time",
+    "5728303e4b864d19001646ab": "Children in Need",
+    "5728303e4b864d19001646ac": "EastEnders",
+    "5728303e4b864d19001646ad": "darkened",
+    "5728303e4b864d19001646ae": "Pulfrich effect",
+    "572831512ca10214002da04a": "Doctor Who and the Curse of Fatal Death",
+    "572831512ca10214002da04b": "four",
+    "572831512ca10214002da04c": "Richard E. Grant, Jim Broadbent, Hugh Grant and Joanna Lumley",
+    "572831512ca10214002da04d": "Joanna Lumley",
+    "572831512ca10214002da04e": "head writer and executive producer",
+    "572833662ca10214002da086": "Star Trek: The Next Generation",
+    "572833662ca10214002da087": "Blue Harvest\" and \"420",
+    "572833662ca10214002da088": "Queer as Folk",
+    "572833662ca10214002da089": "Oliver",
+    "572833662ca10214002da08a": "Brisingr and High Wizardry",
+    "572834524b864d1900164716": "The Chase",
+    "572834524b864d1900164717": "21-minute",
+    "572834524b864d1900164718": "Doctor Who and the Pescatons",
+    "572834524b864d1900164719": "1981",
+    "572834524b864d190016471a": "Slipback",
+    "572835854b864d190016472e": "Fifth, Sixth and Seventh Doctors",
+    "572835854b864d190016472f": "Destiny of the Doctor",
+    "572835854b864d1900164730": "Big Finish Productions",
+    "572835854b864d1900164731": "1999",
+    "572835854b864d1900164732": "2012",
+    "572836732ca10214002da0dc": "1991",
+    "572836732ca10214002da0dd": "mid-sixties",
+    "572836732ca10214002da0de": "since 1979",
+    "572836732ca10214002da0df": "Panini",
+    "572836732ca10214002da0e0": "BBC Books",
+    "572837402ca10214002da0f4": "early 1960s",
+    "572837402ca10214002da0f5": "BBC Television",
+    "572837402ca10214002da0f6": "producers",
+    "572837402ca10214002da0f7": "the BBC",
+    "572838323acd2414000df737": "2006",
+    "572838323acd2414000df738": "2005\u20132010",
+    "572838323acd2414000df739": "2011",
+    "572838323acd2414000df73a": "Michelle Gomez",
+    "572838323acd2414000df73b": "Best Supporting Actress",
+    "57283a392ca10214002da118": "Guinness World Records",
+    "57283a392ca10214002da119": "Doctor Who",
+    "57283a392ca10214002da11a": "electronic",
+    "57283a392ca10214002da11b": "2013",
+    "57283a392ca10214002da11c": "50th anniversary special",
+    "57283b91ff5b5019007d9fa4": "Season 11",
+    "57283b91ff5b5019007d9fa5": "Doctor Who",
+    "57283b91ff5b5019007d9fa6": "third",
+    "57283b91ff5b5019007d9fa7": "SFX",
+    "57283b91ff5b5019007d9fa8": "eight",
+    "57283ce72ca10214002da14c": "Best Drama Series",
+    "57283ce72ca10214002da14d": "five",
+    "57283ce72ca10214002da14e": "25",
+    "57283ce72ca10214002da14f": "2009",
+    "57283ce72ca10214002da150": "a Mind Award",
+    "57283dbeff5b5019007d9fc6": "six",
+    "57283dbeff5b5019007d9fc7": "200",
+    "57283dbeff5b5019007d9fc8": "over a hundred",
+    "57283dbeff5b5019007d9fc9": "Matt Smith",
+    "57283dbeff5b5019007d9fca": "The Waters of Mars",
+    "57283e652ca10214002da166": "Spike Milligan",
+    "57283e652ca10214002da167": "Jon Culshaw",
+    "57283e652ca10214002da168": "a soap sponge",
+    "57283e652ca10214002da16a": "Doctor Who fandom",
+    "57283e652ca10214002da169": "BBC Dead Ringers",
+    "57283c464b864d19001647c8": "private research university",
+    "57283c464b864d19001647c9": "1890",
+    "57283c464b864d19001647ca": "seven",
+    "57283c464b864d19001647cb": "four",
+    "57283c464b864d19001647cc": "5,000",
+    "57283db6ff5b5019007d9fbc": "academic disciplines",
+    "57283db6ff5b5019007d9fbd": "Chicago's physics department",
+    "57283db6ff5b5019007d9fbe": "beneath the university's Stagg Field",
+    "57283db6ff5b5019007d9fbf": "University of Chicago Press",
+    "57283db6ff5b5019007d9fc0": "2020",
+    "57283f014b864d19001647e8": "American Baptist Education Society",
+    "57283f014b864d19001647e9": "John D. Rockefeller",
+    "57283f014b864d19001647ea": "William Rainey Harper",
+    "57283f014b864d19001647eb": "1891",
+    "57283f014b864d19001647ec": "1892",
+    "57284142ff5b5019007da00a": "John D. Rockefeller",
+    "57284142ff5b5019007da00b": "Silas B. Cobb",
+    "57284142ff5b5019007da00c": "Cobb Lecture Hall",
+    "57284142ff5b5019007da00d": "$100,000",
+    "57284142ff5b5019007da00e": "Charles L. Hutchinson",
+    "572843304b864d1900164848": "University of Chicago",
+    "572843304b864d1900164849": "1896",
+    "572843304b864d190016484a": "made a grade of A for all four years",
+    "572843304b864d190016484b": "Several University of Chicago professors",
+    "572843304b864d190016484c": "1896",
+    "57284456ff5b5019007da05c": "Robert Maynard Hutchins",
+    "57284456ff5b5019007da060": "the Common Core",
+    "57284456ff5b5019007da05f": "to emphasize academics over athletics",
+    "57284456ff5b5019007da05e": "24-year",
+    "57284456ff5b5019007da05d": "1929",
+    "57284618ff5b5019007da0a8": "1950s",
+    "57284618ff5b5019007da0a9": "increasing crime and poverty",
+    "57284618ff5b5019007da0aa": "after their second year",
+    "57284618ff5b5019007da0ab": "Hyde Park",
+    "57284618ff5b5019007da0ac": "allowed very young students to attend college",
+    "572847ff3acd2414000df869": "1962",
+    "572847ff3acd2414000df86a": "off-campus rental policies",
+    "572847ff3acd2414000df86b": "1967",
+    "572847ff3acd2414000df86c": "two",
+    "572847ff3acd2414000df86d": "social and political action",
+    "572849b4ff5b5019007da0f2": "mid-2000s",
+    "572849b4ff5b5019007da0f3": "Milton Friedman Institute",
+    "572849b4ff5b5019007da0f4": "$200 million",
+    "572849b4ff5b5019007da0f5": "Chicago Theological Seminary",
+    "572849b4ff5b5019007da0f6": "David G. Booth",
+    "57284b904b864d19001648e2": "Main Quadrangles",
+    "57284b904b864d19001648e3": "six",
+    "57284b904b864d19001648e4": "Cobb, Shepley, Rutan and Coolidge, Holabird & Roche",
+    "57284b904b864d19001648e5": "Magdalen Tower",
+    "57284b904b864d19001648e6": "Christ Church Hall",
+    "57284d484b864d1900164900": "1940s",
+    "57284d484b864d1900164901": "Eero Saarinen",
+    "57284d484b864d1900164902": "School of Social Service Administration",
+    "57284d484b864d1900164903": "Harris School of Public Policy Studies",
+    "57284d484b864d1900164904": "2003",
+    "57284e9fff5b5019007da150": "Singapore, London, and the downtown Streeterville neighborhood of Chicago",
+    "57284e9fff5b5019007da151": "Seine",
+    "57284e9fff5b5019007da152": "2010",
+    "57284e9fff5b5019007da153": "Renmin University",
+    "57284e9fff5b5019007da154": "2015",
+    "5728501aff5b5019007da166": "a board of trustees",
+    "5728501aff5b5019007da167": "50",
+    "5728501aff5b5019007da168": "fourteen",
+    "5728501aff5b5019007da169": "Andrew Alper",
+    "5728501aff5b5019007da16a": "Daniel Diermeier",
+    "5728510f4b864d1900164936": "Higher Learning Commission",
+    "5728510f4b864d1900164937": "four",
+    "5728510f4b864d1900164938": "seven",
+    "57285213ff5b5019007da180": "50",
+    "57285213ff5b5019007da181": "28",
+    "57285213ff5b5019007da182": "five",
+    "57285213ff5b5019007da183": "New Collegiate Division",
+    "572853e8ff5b5019007da188": "the Common Core",
+    "572853e8ff5b5019007da189": "17",
+    "572853e8ff5b5019007da18a": "rigorous, intense",
+    "572853e8ff5b5019007da18b": "Uni in the USA",
+    "572855973acd2414000df925": "University of Chicago Laboratory Schools",
+    "572855973acd2414000df926": "Sonia Shankman Orthogenic School",
+    "572855973acd2414000df927": "four",
+    "572855973acd2414000df928": "public charter schools",
+    "572855973acd2414000df929": "University of Chicago",
+    "572856beff5b5019007da190": "six",
+    "572856beff5b5019007da191": "9.8 million",
+    "572856beff5b5019007da192": "Regenstein Library",
+    "572856beff5b5019007da193": "2011",
+    "572856beff5b5019007da194": "1.3 million",
+    "57285841ff5b5019007da19a": "12",
+    "57285841ff5b5019007da19b": "113",
+    "57285841ff5b5019007da19c": "Oriental Institute",
+    "57285841ff5b5019007da19d": "Fermilab",
+    "57285841ff5b5019007da19e": "Sunspot, New Mexico",
+    "57286010ff5b5019007da1ca": "shaping ideas about the free market",
+    "57286010ff5b5019007da1cb": "Chicago Pile-1",
+    "57286010ff5b5019007da1cc": "Miller\u2013Urey experiment",
+    "57286010ff5b5019007da1cd": "1953",
+    "572861cc4b864d190016495e": "1933",
+    "572861cc4b864d190016495f": "2000",
+    "572861cc4b864d1900164960": "1996",
+    "572861cc4b864d1900164961": "2002",
+    "572861cc4b864d1900164962": "Several thousand",
+    "572863363acd2414000df985": "5,792",
+    "572863363acd2414000df986": "3,468",
+    "572863363acd2414000df987": "5,984",
+    "572863363acd2414000df988": "5,792",
+    "572863363acd2414000df989": "international students",
+    "572864542ca10214002da2de": "University Athletic Association",
+    "572864542ca10214002da2df": "Division III",
+    "572864542ca10214002da2e0": "Big Ten Conference",
+    "572864542ca10214002da2e1": "Jay Berwanger",
+    "572864542ca10214002da2e2": "University President Robert Maynard Hutchins de-emphasized varsity athletics in 1939 and dropped football",
+    "5728659f4b864d190016498a": "400",
+    "5728659f4b864d190016498b": "Recognized Student Organizations",
+    "5728659f4b864d190016498c": "University of Chicago College Bowl Team",
+    "5728659f4b864d190016498d": "Doc Films",
+    "5728659f4b864d190016498e": "the nation's second oldest continuously running student improvisational theater troupe",
+    "5728667eff5b5019007da1fc": "graduate and undergraduate students",
+    "5728667eff5b5019007da1fd": "an Executive Committee",
+    "5728667eff5b5019007da1fe": "two",
+    "5728667eff5b5019007da1ff": "$2 million",
+    "572867543acd2414000df9a1": "fifteen",
+    "572867543acd2414000df9a2": "seven",
+    "572867543acd2414000df9a3": "Alpha Phi Omega",
+    "572867543acd2414000df9a4": "Four",
+    "572867543acd2414000df9a5": "ten",
+    "5728683b3acd2414000df9af": "May",
+    "5728683b3acd2414000df9b0": "1987",
+    "5728683b3acd2414000df9b1": "Festival of the Arts",
+    "5728683b3acd2414000df9b2": "Kuviasungnerk/Kangeiko",
+    "5728683b3acd2414000df9b3": "Summer Breeze",
+    "57286951ff5b5019007da20e": "Satya Nadella",
+    "57286951ff5b5019007da20f": "Satya Nadella",
+    "57286951ff5b5019007da210": "Larry Ellison",
+    "57286951ff5b5019007da211": "Larry Ellison",
+    "57286951ff5b5019007da212": "James O. McKinsey",
+    "57286ab72ca10214002da31e": "Saul Alinsky",
+    "57286ab72ca10214002da31f": "Saul Alinsky, Obama campaign advisor and top political advisor to President Bill Clinton David Axelrod",
+    "57286ab72ca10214002da320": "Robert Bork",
+    "57286ab72ca10214002da321": "Marek Belka",
+    "57286ab72ca10214002da322": "Eliot Ness",
+    "57286bf84b864d19001649d4": "Allan Bloom",
+    "57286bf84b864d19001649d6": "Kurt Vonnegut",
+    "57286bf84b864d19001649d2": "Lauren Oliver",
+    "57286bf84b864d19001649d5": "Susan Sontag, analytic philosopher and Stanford University Professor of Comparative Literature Richard Rorty, and American writer and satirist Kurt Vonnegut",
+    "57286bf84b864d19001649d3": "Philip Roth",
+    "57286d7d4b864d19001649e0": "Philip Glass",
+    "57286d7d4b864d19001649e1": "Katherine Dunham",
+    "57286d7d4b864d19001649e2": "Halo",
+    "57286d7d4b864d19001649e3": "Ed Asner",
+    "57286d7d4b864d19001649e4": "Mike Nichols",
+    "57286ec63acd2414000df9d1": "Carl Sagan",
+    "57286ec63acd2414000df9d2": "John M. Grunsfeld",
+    "57286ec63acd2414000df9d3": "David Suzuki",
+    "57286ec63acd2414000df9d4": "John B. Goodenough",
+    "57286ec63acd2414000df9d5": "Clair Cameron Patterson",
+    "572870b2ff5b5019007da222": "George Stigler",
+    "572870b2ff5b5019007da223": "Margaret Thatcher",
+    "572870b2ff5b5019007da224": "Paul Samuelson",
+    "572870b2ff5b5019007da225": "Eugene Fama",
+    "572871bc4b864d1900164a04": "David Graeber and Donald Johanson",
+    "572871bc4b864d1900164a05": "Samuel Reshevsky",
+    "572871bc4b864d1900164a06": "Samuel P. Huntington",
+    "572872dd2ca10214002da37e": "A. A. Michelson",
+    "572872dd2ca10214002da37f": "Robert A. Millikan",
+    "572872dd2ca10214002da380": "Enrico Fermi",
+    "572872dd2ca10214002da381": "Edward Teller",
+    "572872dd2ca10214002da382": "Maria Goeppert-Mayer",
+    "5728742cff5b5019007da246": "James Henry Breasted",
+    "5728742cff5b5019007da247": "Alberto Calder\u00f3n",
+    "5728742cff5b5019007da248": "Ted Fujita",
+    "5728742cff5b5019007da249": "Glenn T. Seaborg",
+    "5728742cff5b5019007da24a": "Charles Brenton Huggins and Janet Rowley",
+    "5728759cff5b5019007da25b": "Raghuram Rajan",
+    "5728759cff5b5019007da25a": "Goldman Sachs",
+    "5728759cff5b5019007da25c": "David Bevington",
+    "5728759cff5b5019007da25d": "John Mearsheimer and Robert Pape",
+    "5728759cff5b5019007da25e": "Neil Shubin and Paul Sereno",
+    "57285ed5ff5b5019007da1b6": "Yu\u00e1n Ch\u00e1o",
+    "57285ed5ff5b5019007da1b7": "Great Yuan",
+    "57285ed5ff5b5019007da1b8": "Kublai Khan",
+    "57285ed5ff5b5019007da1b9": "Kublai Khan",
+    "57285ed5ff5b5019007da1ba": "1271",
+    "572860e03acd2414000df977": "Mongol Empire",
+    "572860e03acd2414000df978": "Ming dynasty",
+    "572860e03acd2414000df979": "Ming dynasty",
+    "572860e03acd2414000df97a": "Genghis Khan",
+    "57286192ff5b5019007da1dc": "1271",
+    "57286192ff5b5019007da1dd": "Commentaries on the Classic of Changes (I Ching) section regarding Qi\u00e1n (\u4e7e)",
+    "57286192ff5b5019007da1de": "Mongol dynasty",
+    "57286192ff5b5019007da1df": "Great Yuan Great Mongol State",
+    "57286192ff5b5019007da1e0": "Great Khan",
+    "572863c72ca10214002da2d4": "Mongol and Turkic",
+    "572863c72ca10214002da2d5": "1206",
+    "572863c72ca10214002da2d6": "\u00d6gedei Khan",
+    "572863c72ca10214002da2d7": "1206",
+    "572863c72ca10214002da2d8": "his nephew",
+    "572864dd4b864d1900164976": "the Jin",
+    "572864dd4b864d1900164977": "Liu Heima",
+    "572864dd4b864d1900164978": "Khitan",
+    "572864dd4b864d1900164979": "10,000",
+    "572864dd4b864d190016497a": "3",
+    "5728661e2ca10214002da2e8": "Han Chinese",
+    "5728661e2ca10214002da2e9": "Jin",
+    "5728661e2ca10214002da2ea": "Interethnic marriage between Han and Jurchen",
+    "5728661e2ca10214002da2eb": "Shi Bingzhi",
+    "5728661e2ca10214002da2ec": "Song",
+    "572867212ca10214002da2f2": "M\u00f6ngke Khan",
+    "572867212ca10214002da2f3": "southern China",
+    "572867212ca10214002da2f4": "1259",
+    "572867212ca10214002da2f5": "Ariq B\u00f6ke",
+    "572867212ca10214002da2f6": "Zhongtong",
+    "572867d72ca10214002da2fc": "Ogedei",
+    "572867d72ca10214002da2fd": "the south",
+    "572867d72ca10214002da2fe": "Wonjong",
+    "572867d72ca10214002da2ff": "northeast border",
+    "572867d72ca10214002da300": "1262",
+    "572869b84b864d19001649ae": "preserving Mongol interests in China and satisfying the demands of his Chinese subjects",
+    "572869b84b864d19001649b0": "Imperial Secretariat and left the local administrative structure of past Chinese dynasties",
+    "572869b84b864d19001649b2": "Han Chinese",
+    "572869b84b864d19001649b1": "three",
+    "572869b84b864d19001649af": "salt and iron",
+    "57286b003acd2414000df9c1": "Karakorum in Mongolia",
+    "57286b003acd2414000df9c2": "Khanbaliq",
+    "57286b003acd2414000df9c3": "1264",
+    "57286b003acd2414000df9c4": "Zhongdu",
+    "57286b003acd2414000df9c5": "Confucian propriety and ancestor veneration",
+    "57286bb84b864d19001649c8": "commercial, scientific, and cultural",
+    "57286bb84b864d19001649c9": "Mongol peace",
+    "57286bb84b864d19001649ca": "southern China",
+    "57286bb84b864d19001649cb": "Daidu in the north",
+    "57286bb84b864d19001649cc": "Marco Polo",
+    "57286c8cff5b5019007da218": "the Song Emperor",
+    "57286c8cff5b5019007da219": "1115",
+    "57286c8cff5b5019007da21a": "1234",
+    "57286c8cff5b5019007da21b": "Kong Duancao",
+    "57286c8cff5b5019007da21c": "30,000",
+    "57286d4f2ca10214002da328": "northern China",
+    "57286d4f2ca10214002da329": "between 1268 and 1273",
+    "57286d4f2ca10214002da32a": "Yangzi River basin",
+    "57286d4f2ca10214002da32b": "Hangzhou",
+    "57286d4f2ca10214002da32c": "drowned",
+    "57286dfa2ca10214002da332": "1279",
+    "57286dfa2ca10214002da333": "an inauspicious typhoon",
+    "57286dfa2ca10214002da334": "Vietnam \u0110\u1ea1i Vi\u1ec7t",
+    "57286dfa2ca10214002da335": "Battle of B\u1ea1ch \u0110\u1eb1ng",
+    "57286dfa2ca10214002da336": "1288",
+    "57286ead2ca10214002da346": "1253",
+    "57286ead2ca10214002da347": "Zhenjin's third son",
+    "57286ead2ca10214002da348": "1285",
+    "57286ead2ca10214002da349": "Emperor Chengzong",
+    "57286ead2ca10214002da34a": "1294 to 1307",
+    "57286f373acd2414000df9db": "Buyantu Khan",
+    "57286f373acd2414000df9dc": "adopt mainstream Chinese culture",
+    "57286f373acd2414000df9dd": "Li Meng",
+    "57286f373acd2414000df9de": "Department of State Affairs",
+    "57286f373acd2414000df9df": "1313",
+    "57286fa83acd2414000df9e5": "Emperor Gegeen Khan",
+    "57286fa83acd2414000df9e6": "1321 to 1323",
+    "57286fa83acd2414000df9e7": "Baiju",
+    "57286fa83acd2414000df9e8": "the comprehensive institutions of the Great Yuan",
+    "57286fa83acd2414000df9e9": "five",
+    "5728705c2ca10214002da35a": "Shangdu",
+    "5728705c2ca10214002da35b": "the War of the Two Capitals",
+    "5728705c2ca10214002da35c": "four days",
+    "5728705c2ca10214002da35d": "El Tem\u00fcr",
+    "5728705c2ca10214002da35e": "Ragibagh",
+    "5728710c3acd2414000df9ef": "his cultural contribution",
+    "5728710c3acd2414000df9f0": "Academy of the Pavilion of the Star of Literature",
+    "5728710c3acd2414000df9f1": "1329",
+    "5728710c3acd2414000df9f2": "Jingshi Dadian",
+    "5728710c3acd2414000df9f3": "Confucianism",
+    "572871bd3acd2414000dfa03": "1332",
+    "572871bd3acd2414000dfa04": "Emperor Ningzong",
+    "572871bd3acd2414000dfa05": "13",
+    "572871bd3acd2414000dfa06": "nine",
+    "572871bd3acd2414000dfa07": "Liao, Jin, and Song",
+    "572872822ca10214002da374": "struggle, famine, and bitterness",
+    "572872822ca10214002da375": "Kublai Khan's successors lost all influence on other Mongol lands across Asia",
+    "572872822ca10214002da376": "the army and the populace",
+    "572872822ca10214002da377": "Outlaws",
+    "572872822ca10214002da378": "administration",
+    "57287338ff5b5019007da232": "late 1340s",
+    "57287338ff5b5019007da233": "Red Turban Rebellion",
+    "57287338ff5b5019007da234": "fear of betrayal",
+    "57287338ff5b5019007da235": "Red Turban rebels",
+    "57287338ff5b5019007da236": "1368\u20131644",
+    "572878942ca10214002da3a2": "communications between Yuan dynasty and its ally and subordinate in Persia, the Ilkhanate",
+    "572878942ca10214002da3a3": "The Mongols' extensive West Asian and European contacts",
+    "572878942ca10214002da3a4": "Ilkhanate",
+    "572878942ca10214002da3a5": "carrots, turnips, new varieties of lemons, eggplants, and melons, high-quality granulated sugar, and cotton",
+    "572879574b864d1900164a14": "Western musical instruments",
+    "572879574b864d1900164a15": "Tibetan Buddhism",
+    "572879574b864d1900164a16": "Tibetan Buddhism) flourished, although Taoism",
+    "572879574b864d1900164a17": "Confucian",
+    "572879574b864d1900164a18": "travel literature, cartography, geography, and scientific education",
+    "57287b4a4b864d1900164a28": "Marco Polo",
+    "57287b4a4b864d1900164a29": "Cambaluc",
+    "57287b4a4b864d1900164a2a": "the Travels of Marco Polo",
+    "57287b4a4b864d1900164a2b": "Il milione",
+    "57287b4a4b864d1900164a2c": "contact with Persian traders",
+    "57287c142ca10214002da3d0": "Guo Shoujing",
+    "57287c142ca10214002da3d1": "365.2425 days of the year",
+    "57287c142ca10214002da3d2": "granaries",
+    "57287c142ca10214002da3d3": "Beijing",
+    "57287c142ca10214002da3d4": "sorghum",
+    "57287ccb2ca10214002da3da": "non-native Chinese people",
+    "57287ccb2ca10214002da3db": "the Eternal Heaven",
+    "57287ccb2ca10214002da3dc": "Song dynasty and the Ming dynasty",
+    "57287ccb2ca10214002da3dd": "Song dynasty and the Ming dynasty",
+    "57287ccb2ca10214002da3de": "a period of foreign domination",
+    "57287d4a2ca10214002da3e4": "Han Chinese, Khitans, Jurchens, Mongols, and Tibetan Buddhists",
+    "57287d4a2ca10214002da3e5": "Tang, Song",
+    "57287d4a2ca10214002da3e6": "Liu Bingzhong and Yao Shu",
+    "57287d4a2ca10214002da3e7": "traditional Chinese tripartite",
+    "57287d4a2ca10214002da3e8": "civil, military, and censorial offices",
+    "57287ddf3acd2414000dfa3f": "the Privy Council",
+    "57287ddf3acd2414000dfa40": "Sui and Tang dynasties",
+    "57287ddf3acd2414000dfa41": "Mongols and Semuren",
+    "57287ddf3acd2414000dfa42": "native Chinese dynasties",
+    "57287e512ca10214002da3f8": "1269",
+    "57287e512ca10214002da3f9": "Mongolian, Tibetan, and Chinese",
+    "57287e512ca10214002da3fa": "converse well",
+    "57287e512ca10214002da3fb": "Tugh Temur",
+    "57287e512ca10214002da3fc": "Emperor Wenzong",
+    "57287ee3ff5b5019007da274": "1290",
+    "57287ee3ff5b5019007da275": "1291",
+    "57287ee3ff5b5019007da276": "the harvests of their Chinese tenants",
+    "57287f6a3acd2414000dfa51": "painting, mathematics, calligraphy, poetry, and theater",
+    "57287f6a3acd2414000dfa52": "painting, poetry, and calligraphy",
+    "57287f6a3acd2414000dfa53": "Song dynasty",
+    "57287f6a3acd2414000dfa54": "the qu",
+    "57287f6a3acd2414000dfa55": "zaju",
+    "57287fec4b864d1900164a3c": "western khanates",
+    "57287fec4b864d1900164a3d": "Buddhism",
+    "57287fec4b864d1900164a3e": "Tibetan Buddhism",
+    "57287fec4b864d1900164a3f": "Bureau of Buddhist and Tibetan Affairs",
+    "57287fec4b864d1900164a40": "Sakya",
+    "5728804b4b864d1900164a46": "1249",
+    "5728804b4b864d1900164a47": "1314",
+    "5728804b4b864d1900164a48": "matrices",
+    "5728804b4b864d1900164a49": "polynomial algebra",
+    "5728804b4b864d1900164a4a": "1303",
+    "5728809f2ca10214002da40c": "applied mathematics",
+    "5728809f2ca10214002da40d": "cubic interpolation formula",
+    "5728809f2ca10214002da40e": "Shoushi Li",
+    "5728809f2ca10214002da40f": "Shoushi Li",
+    "5728809f2ca10214002da410": "1281",
+    "572881022ca10214002da416": "non-Mongol physicians",
+    "572881022ca10214002da417": "herbal remedies",
+    "572881022ca10214002da418": "herbal remedies",
+    "572881022ca10214002da419": "Imperial Academy of Medicine",
+    "572881022ca10214002da41a": "because it ensured a high income",
+    "572881704b864d1900164a50": "Four Great Schools\" that the Yuan inherited from the Jin dynasty. All four",
+    "572881704b864d1900164a51": "inherited from the Jin dynasty",
+    "572881704b864d1900164a52": "Under the Mongols, the practice of Chinese medicine spread to other parts of the empire",
+    "572881704b864d1900164a53": "acupuncture, moxibustion, pulse diagnosis, and various herbal drugs and elixirs",
+    "572881704b864d1900164a54": "1347",
+    "572881d34b864d1900164a5a": "Muslim medicine",
+    "572881d34b864d1900164a5b": "Jesus the Interpreter",
+    "572881d34b864d1900164a5c": "1263",
+    "572881d34b864d1900164a5d": "humoral system",
+    "572881d34b864d1900164a5e": "yin-yang and wuxing philosophy",
+    "572882242ca10214002da420": "Kingdom of Qocho and Tibetan intermediaries",
+    "572882242ca10214002da421": "Wang Zhen",
+    "572882242ca10214002da422": "12th century",
+    "572882242ca10214002da423": "T\u00f6regene Khatun",
+    "572882242ca10214002da424": "1273",
+    "5728827b2ca10214002da42a": "chao",
+    "5728827b2ca10214002da42b": "mulberry trees",
+    "5728827b2ca10214002da42c": "1275",
+    "5728827b2ca10214002da42d": "woodblocks",
+    "5728827b2ca10214002da42e": "1294",
+    "572883153acd2414000dfa6f": "traditional Chinese autocratic-bureaucratic system",
+    "572883153acd2414000dfa70": "autocratic-bureaucratic system",
+    "572883153acd2414000dfa71": "various allied groups from Central Asia and the western end of the empire",
+    "572883153acd2414000dfa72": "colonial\" coloration. The unequal treatment is possibly due to the fear of transferring power to the ethnic Chinese under their rule",
+    "572883153acd2414000dfa73": "Ilkhanate",
+    "572883a33acd2414000dfa79": "Central Asian Muslims",
+    "572883a33acd2414000dfa7a": "Han Chinese and Khitans from China",
+    "572883a33acd2414000dfa7b": "Bukhara",
+    "572883a33acd2414000dfa7c": "artisans and farmers",
+    "572883a33acd2414000dfa7d": "Qara-Khitay",
+    "57288428ff5b5019007da28e": "restricting Halal slaughter and other Islamic practices",
+    "57288428ff5b5019007da28f": "Kosher butchering",
+    "57288428ff5b5019007da290": "Zhu Yuanzhang",
+    "57288428ff5b5019007da291": "thanks",
+    "57288428ff5b5019007da292": "The Muslims in the semu class",
+    "5728848cff5b5019007da298": "Frederick W. Mote",
+    "5728848cff5b5019007da299": "social power and wealth",
+    "5728848cff5b5019007da29a": "rich and well socially standing",
+    "5728848cff5b5019007da29b": "poverty",
+    "572885023acd2414000dfa83": "southern China",
+    "572885023acd2414000dfa84": "southern China",
+    "572885023acd2414000dfa85": "southern China withstood and fought to the last before caving in",
+    "572885023acd2414000dfa86": "southern China withstood and fought to the last before caving in",
+    "572885023acd2414000dfa87": "private southern Chinese manufacturers and merchants",
+    "5728855d3acd2414000dfa8d": "Uighurs of the Kingdom of Qocho",
+    "5728855d3acd2414000dfa8e": "Karluk Kara-Khanid ruler",
+    "5728855d3acd2414000dfa8f": "Korean King",
+    "5728855d3acd2414000dfa90": "Uighurs surrendered to the Mongols first",
+    "572885c44b864d1900164a78": "The Central Region",
+    "572885c44b864d1900164a79": "Central Secretariat",
+    "572885c44b864d1900164a7a": "Khanbaliq",
+    "572885c44b864d1900164a7b": "Beijing",
+    "572885c44b864d1900164a7c": "Zhongshu Sheng",
+    "5728dab94b864d1900164f96": "Africa",
+    "5728dab94b864d1900164f97": "East African Community",
+    "5728dab94b864d1900164f98": "Nairobi",
+    "5728dab94b864d1900164f99": "Tanzania",
+    "5728dab94b864d1900164f9a": "45 million",
+    "5728de0e2ca10214002da9dc": "tropical",
+    "5728de0e2ca10214002da9dd": "cooler",
+    "5728de0e2ca10214002da9de": "Mount Kenya",
+    "5728de0e2ca10214002da9df": "Somalia and Ethiopia",
+    "5728de0e2ca10214002da9e0": "its safaris",
+    "5728ef8d2ca10214002daac2": "since the Lower Paleolithic period",
+    "5728ef8d2ca10214002daac3": "first millennium AD",
+    "5728ef8d2ca10214002daac4": "Bantu and Nilotic",
+    "5728ef8d2ca10214002daac5": "19th century",
+    "5728ef8d2ca10214002daac6": "December 1963",
+    "5728f5376aef0514001548c4": "Mount Kenya",
+    "5728f5376aef0514001548c5": "Kikuyu, Embu and Kamba",
+    "5728f5376aef0514001548c6": "God's resting place",
+    "5728f9342ca10214002dab52": "Kenia and Kegnia",
+    "5728f9342ca10214002dab53": "correct African pronunciation",
+    "5728f9342ca10214002dab54": "Joseph Thompsons",
+    "5728f9342ca10214002dab55": "1862",
+    "5728f9cf4b864d1900165166": "lion, leopard, buffalo, rhinoceros, and elephant",
+    "5728f9cf4b864d1900165167": "lion, leopard, buffalo, rhinoceros, and elephant",
+    "5728f9cf4b864d1900165168": "Kenya and in the Masai Mara",
+    "5728f9cf4b864d1900165169": "between June and September",
+    "5728f9cf4b864d190016516a": "2,900 kilometres (1,802 mi)",
+    "5728fa576aef051400154920": "more than 20 million years ago",
+    "5728fa576aef051400154921": "Pleistocene epoch",
+    "5728fa576aef051400154922": "Kamoya Kimeu",
+    "5728fa576aef051400154923": "1.6-million",
+    "5728fa576aef051400154924": "Mary Leakey and Louis Leakey",
+    "5728fd9b3f37b31900477f3b": "The Swahili",
+    "5728fd9b3f37b31900477f3c": "Mombasa",
+    "5728fd9b3f37b31900477f3d": "Duarte Barbosa",
+    "5728fea1af94a219006a9ef5": "Kenyan Coast",
+    "5728fea1af94a219006a9ef6": "Malindi",
+    "5728fea1af94a219006a9ef7": "14th century",
+    "5729024f1d04691400778f5f": "August 1914",
+    "5729024f1d04691400778f60": "British East Africa (as the Protectorate was generally known) and German East Africa",
+    "5729024f1d04691400778f61": "Lt Col Paul von Lettow-Vorbeck",
+    "5729024f1d04691400778f62": "guerrilla warfare campaign",
+    "5729024f1d04691400778f63": "Northern Rhodesia",
+    "572903d96aef0514001549a4": "The central highlands",
+    "572903d96aef0514001549a5": "itinerant farmers",
+    "572903d96aef0514001549a6": "banned the growing of coffee",
+    "572903d96aef0514001549a7": "80,000",
+    "5729046aaf94a219006a9f4d": "15 January 1954",
+    "5729046aaf94a219006a9f4e": "The operation effectively placed Nairobi under military siege",
+    "5729046aaf94a219006a9f4f": "24 April 1954",
+    "5729046aaf94a219006a9f50": "4,686",
+    "5729046aaf94a219006a9f51": "Swynnerton Plan",
+    "572906e23f37b31900477f8d": "1957",
+    "572906e23f37b31900477f8e": "Kenya African National Union (KANU) of Jomo Kenyatta",
+    "572906e23f37b31900477f8f": "12 December 1963",
+    "572906e23f37b31900477f90": "Kenya Independence Act 1963",
+    "572906e23f37b31900477f91": "Republic of Kenya",
+    "572909406aef0514001549dc": "queuing",
+    "572909406aef0514001549dd": "widespread agitation for constitutional reform",
+    "572909406aef0514001549de": "Daniel arap Moi",
+    "572909ebaf94a219006a9fc5": "presidential representative democratic republic",
+    "572909ebaf94a219006a9fc6": "head of state and head of government",
+    "572909ebaf94a219006a9fc7": "the government",
+    "572909ebaf94a219006a9fc8": "the government and the National Assembly and the Senate",
+    "572909ebaf94a219006a9fc9": "Judiciary",
+    "57290b21af94a219006a9fcf": "low",
+    "57290b21af94a219006a9fd0": "prevalence of public sector corruption",
+    "57290b21af94a219006a9fd1": "139th",
+    "57290b21af94a219006a9fd2": "the establishment of a new and independent Ethics and Anti-Corruption Commission (EACC)",
+    "57290d811d04691400778fcf": "Party of National Unity",
+    "57290d811d04691400778fd0": "Orange Democratic Movement (ODM)",
+    "57290d811d04691400778fd1": "complicity",
+    "57290d811d04691400778fd2": "Odinga",
+    "57290e153f37b31900477fd9": "programmes",
+    "57290e153f37b31900477fda": "the Truth, Justice and Reconciliation Commission",
+    "57290e153f37b31900477fdb": "Evangelical Lutheran Church in Kenya",
+    "57290e153f37b31900477fdc": "Kenya National Dialogue and Reconciliation process",
+    "57290ee2af94a219006a9fff": "28 February 2008",
+    "57290ee2af94a219006aa000": "second Prime Minister",
+    "57290ee2af94a219006aa001": "PNU and ODM camps",
+    "57290ee2af94a219006aa002": "depending on each party's strength in Parliament",
+    "57290ee2af94a219006aa003": "until the end of the current Parliament",
+    "57290f963f37b31900477feb": "the Government",
+    "57290f963f37b31900477fec": "Jakaya Kikwete",
+    "57290f963f37b31900477fed": "Nairobi's Harambee House",
+    "57290f963f37b31900477fee": "29 February 2008",
+    "57290f963f37b31900477fef": "the two political parties would share power equally",
+    "572913626aef051400154a30": "would eliminate the position of Prime Minister and simultaneously reduce the powers of the President",
+    "572913626aef051400154a31": "4 August 2010",
+    "572913626aef051400154a32": "bill of rights",
+    "572913626aef051400154a33": "27 August 2010",
+    "572913626aef051400154a34": "the Second Republic",
+    "572914441d04691400779025": "December 2014",
+    "572914441d04691400779026": "it infringed on democratic freedoms",
+    "572914441d04691400779027": "Opposition politicians, human rights groups, and nine Western countries",
+    "572914441d04691400779028": "it infringed on democratic freedoms",
+    "572914441d04691400779029": "United States, Britain, Germany and France",
+    "572914f46aef051400154a46": "US President Barack Obama",
+    "572914f46aef051400154a47": "Barack Obama",
+    "572914f46aef051400154a48": "China",
+    "572914f46aef051400154a49": "July 2015",
+    "572915621d0469140077902f": "peacekeeping",
+    "572915621d04691400779030": "counter-insurgency operations",
+    "572915621d04691400779031": "human rights violations",
+    "572915e43f37b31900478005": "Kenya\u2019s armed forces",
+    "572915e43f37b31900478006": "Because the operations of the armed forces have been traditionally cloaked by the ubiquitous blanket of \u201cstate security\u201d",
+    "572915e43f37b31900478007": "credible claims of corruption were made with regard to recruitment  and procurement of Armoured Personnel Carriers",
+    "572915e43f37b31900478008": "the wisdom and prudence of certain decisions of procurement",
+    "572916f16aef051400154a56": "Human Development Index",
+    "572916f16aef051400154a57": "Kenya",
+    "572916f16aef051400154a58": "less than $1.25",
+    "572916f16aef051400154a59": "frontier market",
+    "572917743f37b3190047800d": "rapid expansion in telecommunication and financial activity",
+    "572917743f37b3190047800f": "food security",
+    "572917743f37b31900478010": "Industry and manufacturing",
+    "572917743f37b3190047800e": "75%",
+    "572917ff6aef051400154a5e": "61%",
+    "572917ff6aef051400154a5f": "tourism",
+    "572917ff6aef051400154a60": "steady growth",
+    "572917ff6aef051400154a62": "coastal beaches and the game reserves",
+    "572917ff6aef051400154a61": "Germany and the United Kingdom",
+    "572918bd3f37b31900478016": "24%",
+    "572918bd3f37b31900478017": "tea, horticultural produce, and coffee",
+    "572918bd3f37b31900478015": "Agriculture",
+    "572918bd3f37b31900478018": "weather",
+    "57291a7b1d0469140077903f": "International Crops Research Institute for the Semi-Arid Tropics",
+    "57291a7b1d04691400779040": "drought resistant",
+    "57291a7b1d04691400779041": "by stimulating the growth of local seed production and agro-dealer networks for distribution and marketing",
+    "57291a7b1d04691400779042": "increase local producer prices by 20\u201325%",
+    "57291a7b1d04691400779043": "local producer prices",
+    "57291b461d04691400779049": "fertile highlands",
+    "57291b461d0469140077904a": "Tea, coffee, sisal, pyrethrum, corn, and wheat",
+    "57291b461d0469140077904b": "semi-arid savanna",
+    "57291b461d0469140077904c": "53%",
+    "57291b461d0469140077904d": "Kenyans for Kenya",
+    "57291beb1d04691400779053": "Kenya",
+    "57291beb1d04691400779054": "14%",
+    "57291beb1d04691400779055": "Nairobi, Mombasa and Kisumu",
+    "57291beb1d04691400779056": "small-scale manufacturing of household goods, motor-vehicle parts, and farm implements",
+    "57291d9e3f37b31900478035": "Kenya's inclusion among the beneficiaries of the US Government's African Growth and Opportunity Act",
+    "57291d9e3f37b31900478036": "2000",
+    "57291dfaaf94a219006aa09b": "hydroelectric stations",
+    "57291dfaaf94a219006aa09c": "upper Tana River",
+    "57291dfaaf94a219006aa09d": "1997",
+    "57291f153f37b31900478043": "Turkana",
+    "57291f153f37b31900478044": "10 billion",
+    "57291f153f37b31900478045": "Exploration",
+    "57291f153f37b31900478046": "20% to 25%",
+    "57291fb4af94a219006aa0b1": "$474 million",
+    "57291fb4af94a219006aa0b2": "$474 million, representing Kenya's largest source of foreign direct investment",
+    "57291fb4af94a219006aa0b3": "gain support from China for a planned $2.5 billion railway from the southern Kenyan port of Mombasa to neighboring Uganda",
+    "57291fb4af94a219006aa0b4": "Base Titanium, a subsidiary of Base resources of Australia, shipped its first major consignment of minerals to China",
+    "57291fb4af94a219006aa0b5": "environmental and social problems",
+    "57292046af94a219006aa0bb": "climate",
+    "57292046af94a219006aa0bc": "an economic development programme",
+    "57292046af94a219006aa0bd": "Vision 2030",
+    "57292046af94a219006aa0be": "an economic development programme it hopes will put the country in the same league as the Asian Economic Tigers by the year 2030",
+    "57292046af94a219006aa0bf": "climate will be a central issue in the renewed Medium Term Plan",
+    "572920d73f37b31900478055": "agriculture",
+    "572920d73f37b31900478056": "30%",
+    "572920d73f37b31900478057": "9\u201318",
+    "572920d73f37b31900478058": "poverty, the lack of access to education and weak government institutions",
+    "572921646aef051400154a78": "Kenyan English",
+    "572921646aef051400154a79": "English and Swahili",
+    "572921646aef051400154a7a": "commerce, schooling and government",
+    "572921646aef051400154a7b": "the country",
+    "572922206aef051400154a8a": "Christian",
+    "572922206aef051400154a8b": "Protestant",
+    "572922206aef051400154a8c": "3 million",
+    "572922206aef051400154a8d": "Nairobi",
+    "57292449af94a219006aa0dd": "2.4%",
+    "57292449af94a219006aa0de": "Sixty percent",
+    "57292449af94a219006aa0df": "Christian",
+    "57292449af94a219006aa0e0": "300,000",
+    "572924b53f37b31900478067": "Nurses",
+    "572924b53f37b31900478068": "clinical officers, medical officers and medical practitioners",
+    "572924b53f37b31900478069": "65,000",
+    "572924b53f37b3190047806a": "7,000",
+    "572925491d046914007790c3": "Diseases of poverty",
+    "572925491d046914007790c4": "Half",
+    "572925491d046914007790c5": "malaria, HIV/AIDS, pneumonia, diarrhoea and malnutrition",
+    "572925491d046914007790c6": "weak policies, corruption, inadequate health workers, weak management and poor leadership in the public health sector",
+    "572925491d046914007790c7": "15 million",
+    "572925a81d046914007790cd": "British colonists",
+    "572925a81d046914007790ce": "12 December 1963",
+    "572925a81d046914007790cf": "Ominde Commission",
+    "572925a81d046914007790d0": "introduce changes that would reflect the nation's sovereignty",
+    "572925a81d046914007790d1": "7\u20134\u20132\u20133 system",
+    "572926086aef051400154ac2": "look at both the possibilities of setting up a second university in Kenya",
+    "572926086aef051400154ac3": "an 8\u20134\u20134 system",
+    "572926086aef051400154ac4": "8\u20134\u20134 system",
+    "572926086aef051400154ac5": "1992",
+    "572926653f37b31900478079": "January 1985",
+    "572926653f37b3190047807a": "vocational subjects",
+    "572926653f37b3190047807b": "the new structure would enable school drop-outs at all levels either to be self-employed or to secure employment in the informal sector",
+    "572926653f37b3190047807c": "January 2003",
+    "572926653f37b3190047807d": "70%",
+    "572926d23f37b31900478083": "six years",
+    "572926d23f37b31900478084": "12 years",
+    "572926d23f37b31900478085": "join a vocational youth/village polytechnic",
+    "572926d23f37b31900478086": "join a polytechnic or other technical college",
+    "5729276c1d046914007790d7": "85%",
+    "5729276c1d046914007790d8": "three to five",
+    "5729276c1d046914007790d9": "admission to Standard One (First Grade)",
+    "5729276c1d046914007790da": "those who proceed to secondary school or vocational training",
+    "5729276c1d046914007790db": "Kenya Certificate of Secondary Education",
+    "572927d06aef051400154ade": "Kenya National Library Service",
+    "572927d06aef051400154adf": "establish, equip, manage and maintain national and public libraries in the country",
+    "572927d06aef051400154ae0": "peoples university",
+    "572927d06aef051400154ae1": "it is open to all irrespective of age, literacy level and has materials relevant to people of all walks of life",
+    "5729281baf94a219006aa11f": "cricket, rallying, football, rugby union and boxing",
+    "5729281baf94a219006aa120": "dominance in middle-distance and long-distance athletics",
+    "5729281baf94a219006aa121": "Kenyan athletes",
+    "5729281baf94a219006aa122": "Morocco and Ethiopia",
+    "572928bf6aef051400154af0": "six",
+    "572928bf6aef051400154af1": "several medals",
+    "572928bf6aef051400154af2": "IAAF Golden League jackpot",
+    "572928bf6aef051400154af3": "the defection of a number of Kenyan athletes to represent other countries",
+    "572928bf6aef051400154af4": "economic or financial factors",
+    "5729293d3f37b3190047809f": "volleyball",
+    "5729293d3f37b319004780a0": "Cricket",
+    "5729293d3f37b319004780a1": "2003",
+    "5729293d3f37b319004780a2": "Rakep Patel",
+    "5729293d3f37b319004780a3": "March 2007",
+    "57292994af94a219006aa131": "Safari Rally",
+    "57292994af94a219006aa132": "Safari Rally",
+    "57292994af94a219006aa133": "Bj\u00f6rn Waldeg\u00e5rd, Hannu Mikkola, Tommi M\u00e4kinen, Shekhar Mehta, Carlos Sainz and Colin McRae",
+    "572929d56aef051400154b0a": "three",
+    "572929d56aef051400154b0b": "10 o'clock",
+    "572929d56aef051400154b0c": "tea or porridge with bread, chapati, mahamri, boiled sweet potatoes or yams",
+    "572929d56aef051400154b0d": "Ugali",
+    "57293b843f37b31900478133": "United Nations",
+    "57293b843f37b31900478134": "the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP)",
+    "57293b843f37b31900478135": "greenhouse gas concentrations in the atmosphere",
+    "57293b843f37b31900478136": "United Nations Framework Convention on Climate Change",
+    "57293b843f37b31900478137": "43/53",
+    "57293bc91d0469140077919b": "Hoesung Lee",
+    "57293bc91d0469140077919c": "Korean",
+    "57293bc91d0469140077919d": "Ismail El Gizouli",
+    "57293bc91d0469140077919e": "Hoesung Lee",
+    "57293bc91d0469140077919f": "February 2015",
+    "57293c246aef051400154bb8": "representatives appointed by governments and organizations",
+    "57293c246aef051400154bb9": "322",
+    "57293c246aef051400154bba": "Non Governmental and Intergovernmental Organizations",
+    "57293c246aef051400154bbb": "seven-eighths",
+    "57293ca73f37b3190047815b": "1989",
+    "57293ca73f37b3190047815c": "United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO)",
+    "57293ca73f37b3190047815e": "United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO)",
+    "57293ca73f37b3190047815f": "Financial Regulations",
+    "57293ca73f37b3190047815d": "United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO)",
+    "57293d116aef051400154bc8": "carry out research",
+    "57293d116aef051400154bc9": "published sources",
+    "57293d116aef051400154bca": "non-peer-reviewed sources",
+    "57293d116aef051400154bcb": "model results, reports from government agencies and non-governmental organizations, and industry journals",
+    "57293d6d1d046914007791b7": "two",
+    "57293d6d1d046914007791b8": "ten to fifteen",
+    "57293d6d1d046914007791b9": "two",
+    "57293d6d1d046914007791ba": "The coordinating lead authors",
+    "57293d6d1d046914007791bb": "Working Group chairs",
+    "57293e221d046914007791d5": "emissions resulting from human activities are substantially increasing the atmospheric concentrations",
+    "57293e221d046914007791d6": "an additional warming of the Earth's surface",
+    "57293e221d046914007791d7": "over half",
+    "57293e221d046914007791d8": "business as usual",
+    "57293e221d046914007791d9": "0.3 to 0.6 \u00b0C",
+    "57293e983f37b3190047818b": "2001",
+    "57293e983f37b3190047818c": "16",
+    "57293e983f37b3190047818d": "Science",
+    "57293e983f37b3190047818e": "90%",
+    "57293e983f37b3190047818f": "between 1.4 and 5.8 \u00b0C above 1990 levels",
+    "57293f353f37b3190047819b": "Richard Lindzen",
+    "57293f353f37b3190047819c": "does not faithfully summarize the full WGI report",
+    "57293f353f37b3190047819d": "John Houghton",
+    "57293f353f37b3190047819e": "co-chair",
+    "57293f353f37b3190047819f": "scientific evidence",
+    "57293f8a6aef051400154bde": "follows the same procedures as for IPCC Assessment Reports",
+    "57293f8a6aef051400154bdf": "2011",
+    "57293f8a6aef051400154be0": "2011",
+    "57293f8a6aef051400154be1": "governments",
+    "572940246aef051400154bec": "Data Distribution Centre and the National Greenhouse Gas Inventories Programme",
+    "572940246aef051400154bed": "default emission factors",
+    "572940246aef051400154bee": "fuel consumption, industrial production",
+    "572940246aef051400154bef": "WMO Executive Council",
+    "572940973f37b319004781a5": "the date",
+    "572940973f37b319004781a6": "poor application of well-established IPCC procedures",
+    "572940973f37b319004781a7": "an ICSI report",
+    "572940973f37b319004781a8": "WWF report",
+    "572941273f37b319004781ad": "Former IPCC chairman",
+    "572941273f37b319004781ae": "the direction of making it seem like climate change is more serious by overstating the impact",
+    "572941273f37b319004781af": "climate expert",
+    "572941273f37b319004781b0": "climate change",
+    "572941273f37b319004781b1": "generally unfounded and also marginal",
+    "57294209af94a219006aa201": "1999",
+    "57294209af94a219006aa202": "Michael E. Mann, Raymond S. Bradley and Malcolm K. Hughes",
+    "57294209af94a219006aa203": "hockey stick graph",
+    "57294209af94a219006aa204": "Jones et al. 1998, Pollack, Huang & Shen 1998, Crowley & Lowery 2000 and Briffa 2000",
+    "57294279af94a219006aa209": "between 1000 and 1900",
+    "57294279af94a219006aa20a": "Fred Singer",
+    "57294279af94a219006aa20b": "Capitol Hill, Washington, D.C.",
+    "57294279af94a219006aa20c": "18 July 2000",
+    "57294279af94a219006aa20d": "Commerce, Science and Transportation",
+    "572943ab1d04691400779219": "Rep. Joe Barton",
+    "572943ab1d0469140077921a": "Ed Whitfield",
+    "572943ab1d0469140077921b": "23 June 2005",
+    "572943ab1d0469140077921c": "Sherwood Boehlert",
+    "572943ab1d0469140077921d": "Sherwood Boehlert",
+    "572944e03f37b319004781e2": "2007",
+    "572944e03f37b319004781e1": "2007",
+    "572944e03f37b319004781e4": "Ten",
+    "572944e03f37b319004781e5": "divergence",
+    "572944e03f37b319004781e3": "14",
+    "572945b11d0469140077922f": "1 February 2007",
+    "572945b11d04691400779230": "compared IPCC 2001 projections on temperature and sea level change with observations",
+    "572945b11d04691400779231": "compared IPCC 2001 projections on temperature and sea level change with observations",
+    "572945b11d04691400779232": "compared IPCC 2001 projections on temperature and sea level change with observations",
+    "572949306aef051400154c68": "projected rises in sea levels",
+    "572949306aef051400154c69": "0.5\u20131.4 m",
+    "572949306aef051400154c6a": "0.5\u20131.4 m",
+    "572949306aef051400154c6b": "2001",
+    "57294baaaf94a219006aa26b": "coordinating lead author",
+    "57294baaaf94a219006aa26c": "Science Magazine",
+    "57294baaaf94a219006aa26d": "consensus",
+    "57294e6b1d04691400779275": "IPCC process on climate change",
+    "57294e6b1d04691400779276": "Climate Change",
+    "57294e6b1d04691400779277": "states and governments",
+    "5729506d6aef051400154cac": "Sheldon Ungar",
+    "5729506d6aef051400154cad": "varying regional cost-benefit analysis and burden-sharing conflicts with regard to the distribution of emission reductions",
+    "5729506d6aef051400154cae": "regional burden sharing conflicts",
+    "5729506d6aef051400154caf": "the UK government",
+    "5729517d6aef051400154cc8": "scientific papers",
+    "5729517d6aef051400154cc9": "significant new evidence or events that change our understanding of climate science",
+    "5729517d6aef051400154cca": "IPCC",
+    "572951f16aef051400154cce": "five",
+    "572951f16aef051400154ccf": "Nature",
+    "572951f16aef051400154cd0": "the panel employ a full-time staff and remove government oversight from its processes to avoid political interference",
+    "572951f16aef051400154cd1": "remove government oversight from its processes",
+    "572953013f37b3190047824d": "to conduct photosynthesis",
+    "572953013f37b3190047824e": "energy",
+    "572953013f37b3190047824f": "oxygen",
+    "572953013f37b31900478250": "Calvin cycle",
+    "572953013f37b31900478251": "1",
+    "5729544c3f37b31900478257": "pinch in two",
+    "5729544c3f37b31900478258": "environmental factors like light color and intensity",
+    "5729544c3f37b31900478259": "contain their own DNA",
+    "5729544c3f37b3190047825a": "a photosynthetic cyanobacterium",
+    "5729544c3f37b3190047825b": "they circulate and are moved around within plant cells",
+    "572956c86aef051400154d1a": "plants and algae",
+    "572956c86aef051400154d1b": "Russian",
+    "572956c86aef051400154d1c": "biologist",
+    "572956c86aef051400154d1d": "1905",
+    "572956c86aef051400154d1e": "Andreas Schimper",
+    "572957361d046914007792cf": "Cyanobacteria",
+    "572957361d046914007792d0": "bacteria",
+    "572957361d046914007792d2": "they have two cell membranes",
+    "572957361d046914007792d3": "peptidoglycan",
+    "572957361d046914007792d1": "blue-green algae",
+    "572957ad1d046914007792d9": "eukaryotic",
+    "572957ad1d046914007792da": "around a billion years ago",
+    "572957ad1d046914007792db": "lipid-bilayer membranes",
+    "572957ad1d046914007792dc": "phagosomal membrane",
+    "572957ad1d046914007792dd": "many of its genes were lost or transferred to the nucleus of the host",
+    "5729582b1d046914007792e3": "almost the same thing as chloroplast",
+    "5729582b1d046914007792e4": "three",
+    "5729582b1d046914007792e5": "red algal chloroplast lineage",
+    "5729582b1d046914007792e6": "green chloroplast lineage",
+    "5729582b1d046914007792e7": "green chloroplast",
+    "572958cc6aef051400154d2a": "glaucophyte",
+    "572958cc6aef051400154d2b": "glaucophyte",
+    "572958cc6aef051400154d2c": "glaucophyte chloroplasts",
+    "572958cc6aef051400154d2d": "carboxysome",
+    "572958cc6aef051400154d2e": "icosahedral",
+    "57295a116aef051400154d44": "chlorophyll a and phycobilins",
+    "57295a116aef051400154d45": "phycobilisomes",
+    "57295a116aef051400154d46": "phycobilin phycoerytherin",
+    "57295a116aef051400154d47": "catch more sunlight in deep water",
+    "57295a116aef051400154d48": "starch",
+    "57295b5b1d04691400779315": "phycobilisomes",
+    "57295b5b1d04691400779316": "accessory pigments that override the chlorophylls' green colors",
+    "57295b5b1d04691400779317": "peptidoglycan wall between their double membrane, and have replaced it with an intermembrane space",
+    "57295b5b1d04691400779318": "chloroplast division",
+    "57295b5b1d04691400779319": "chlorophyll b",
+    "572961f61d04691400779359": "double membrane",
+    "572961f61d0469140077935a": "outside of the original two",
+    "572961f61d0469140077935b": "when a nonphotosynthetic eukaryote engulfed a chloroplast-containing alga but failed to digest it",
+    "572961f61d0469140077935c": "outside of the original two",
+    "572961f61d0469140077935d": "its chloroplast",
+    "572962953f37b319004782f5": "a pyrenoid and thylakoids",
+    "572962953f37b319004782f6": "flagellated",
+    "572962953f37b319004782f7": "stacked in groups of three",
+    "572962953f37b319004782f8": "Starch",
+    "572962953f37b319004782f9": "the membrane of the primary endosymbiont",
+    "572963221d04691400779385": "cryptomonads",
+    "572963221d04691400779386": "four membranes",
+    "572963221d04691400779387": "a nucleomorph",
+    "572963221d04691400779388": "granules found in the periplastid space",
+    "572963221d04691400779389": "in stacks of two",
+    "572963876aef051400154dd2": "helicosproidia",
+    "572963876aef051400154dd3": "chromalveolates",
+    "572963876aef051400154dd4": "malaria parasite",
+    "572963876aef051400154dd5": "red algal derived chloroplast",
+    "572963876aef051400154dd6": "amylopectin starch granules",
+    "5729645b3f37b31900478321": "fatty acids",
+    "5729645b3f37b31900478322": "apicomplexan-related",
+    "5729645b3f37b31900478323": "isopentenyl pyrophosphate synthesis",
+    "5729645b3f37b31900478324": "all photosynthetic function",
+    "5729645b3f37b31900478325": "four",
+    "572965566aef051400154e00": "carotenoid pigment",
+    "572965566aef051400154e01": "chloroplasts",
+    "572965566aef051400154e02": "triplet-stacked",
+    "572965566aef051400154e03": "red algal endosymbiont's original cell membrane",
+    "572965e73f37b3190047832b": "fucoxanthin dinophyte",
+    "572965e73f37b3190047832c": "fucoxanthin dinophyte",
+    "572965e73f37b3190047832d": "four",
+    "572965e73f37b3190047832e": "a six membraned chloroplast",
+    "572966626aef051400154e13": "cryptophyte",
+    "572966626aef051400154e14": "old ones",
+    "572966626aef051400154e12": "phycobilin-containing chloroplast taken from a cryptophyte",
+    "572966626aef051400154e15": "a two-membraned chloroplast",
+    "572966ebaf94a219006aa391": "heterokontophyte",
+    "572966ebaf94a219006aa392": "diatom",
+    "572966ebaf94a219006aa393": "five",
+    "572966ebaf94a219006aa394": "whether you count the entire diatom endosymbiont as the chloroplast",
+    "572966ebaf94a219006aa395": "granules",
+    "572967e31d046914007793b1": "dinophyte nucleus",
+    "572967e31d046914007793b2": "Lepidodinium",
+    "572967e31d046914007793b3": "peridinin chloroplast",
+    "572967e31d046914007793b4": "green algal derived chloroplast",
+    "572967e31d046914007793b5": "green algal derived chloroplast",
+    "5729686d1d046914007793c1": "first set of endosymbiotic events",
+    "5729686d1d046914007793c2": "photosynthetic cyanobacterial endosymbiont",
+    "5729686d1d046914007793c3": "a million",
+    "5729686d1d046914007793c4": "850",
+    "5729686d1d046914007793c5": "three million",
+    "572968cf1d046914007793cb": "ctDNA, or cpDNA",
+    "572968cf1d046914007793cc": "plastome",
+    "572968cf1d046914007793cd": "1962",
+    "572968cf1d046914007793ce": "1986",
+    "572968cf1d046914007793cf": "Japanese research teams",
+    "57296977af94a219006aa3bd": "chloroplast DNAs which have lost some of the inverted repeat segments",
+    "57296977af94a219006aa3be": "direct repeats",
+    "57296977af94a219006aa3bf": "stabilize the rest of the chloroplast genome",
+    "572969f51d046914007793dd": "electron microscopy",
+    "572969f51d046914007793de": "two",
+    "572969f51d046914007793e0": "double displacement loop",
+    "572969f51d046914007793df": "double displacement loop",
+    "572969f51d046914007793e1": "rolling circle mechanism",
+    "57296a65af94a219006aa3c3": "A \u2192 G deamination",
+    "57296a65af94a219006aa3c4": "single stranded",
+    "57296a65af94a219006aa3c5": "linear",
+    "57296a65af94a219006aa3c6": "through homologous recombination",
+    "57296a65af94a219006aa3c7": "circular chromosomes",
+    "57296ab93f37b31900478369": "bacteriophage T4",
+    "57296ab93f37b3190047836a": "linear",
+    "57296ab93f37b3190047836b": "circular",
+    "57296ab93f37b3190047836c": "via a D loop mechanism",
+    "57296b151d046914007793f1": "Endosymbiotic gene transfer",
+    "57296b151d046914007793f2": "lost chloroplast's existence",
+    "57296b151d046914007793f3": "heterokontophyte",
+    "57296b151d046914007793f4": "red algal derived chloroplast",
+    "57296bf96aef051400154e52": "nonfunctional pseudogenes",
+    "57296bf96aef051400154e53": "around half",
+    "57296bf96aef051400154e54": "participating in cell division, protein routing, and even disease resistance",
+    "57296bf96aef051400154e55": "cell membrane",
+    "57296c5c3f37b3190047837f": "a ribosome in the cytosol",
+    "57296c5c3f37b31900478380": "on a ribosome in the cytosol",
+    "57296c5c3f37b31900478381": "helps many proteins bind the polypeptide",
+    "57296c5c3f37b31900478382": "keeping it from folding prematurely",
+    "57296cb21d04691400779403": "lens-shaped",
+    "57296cb21d04691400779404": "5\u20138 \u03bcm",
+    "57296cb21d04691400779405": "1\u20133 \u03bcm",
+    "57296cb21d04691400779406": "a net",
+    "57296cb21d04691400779407": "a cup",
+    "57296d1b1d0469140077940d": "chloroplasts are surrounded by a double membrane",
+    "57296d1b1d0469140077940e": "the product of the host's cell membrane infolding to form a vesicle to surround the ancestral cyanobacterium",
+    "57296d1b1d0469140077940f": "homologous",
+    "57296d8d1d0469140077941d": "mitochondrial double membrane",
+    "57296d8d1d0469140077941e": "run proton pumps",
+    "57296d8d1d0469140077941f": "generate ATP energy",
+    "57296d8d1d04691400779420": "internal thylakoid system",
+    "57296d8d1d04691400779421": "double membrane",
+    "57296de03f37b3190047839b": "Stromules",
+    "57296de03f37b3190047839c": "stroma-containing tubule",
+    "57296de03f37b3190047839d": "to increase the chloroplast's surface area for cross-membrane transport",
+    "57296de03f37b3190047839e": "1962",
+    "57296e43af94a219006aa3e5": "chloroplasts of C4 plants",
+    "57296e43af94a219006aa3e6": "chloroplasts of C4 plants",
+    "57296e43af94a219006aa3e7": "The chloroplast peripheral reticulum",
+    "57296e43af94a219006aa3e8": "increase the chloroplast's surface area for cross-membrane transport between its stroma and the cell cytoplasm",
+    "57296e43af94a219006aa3e9": "thylakoids and intermembrane space",
+    "57296eb01d04691400779435": "to synthesize a small fraction of their proteins",
+    "57296eb01d04691400779436": "two-thirds",
+    "57296eb01d04691400779437": "two-thirds",
+    "57296eb01d04691400779438": "motifs for shine-dalgarno sequence recognition",
+    "57296eb01d04691400779439": "translation initiation",
+    "57296eee6aef051400154e8e": "plastoglobulus",
+    "57296eee6aef051400154e8f": "spherical",
+    "57296eee6aef051400154e90": "lipids and proteins",
+    "57296eee6aef051400154e91": "45\u201360 nanometers across",
+    "57296eee6aef051400154e92": "a lipid monolayer",
+    "57296f3caf94a219006aa3fb": "a thylakoid",
+    "57296f3caf94a219006aa3fc": "thylakoid network",
+    "57296f3caf94a219006aa3fd": "attached directly to their parent thylakoid",
+    "57296f3caf94a219006aa3fe": "old or stressed chloroplasts",
+    "57296f85af94a219006aa403": "The chloroplasts of some hornworts and algae contain structures called pyrenoids. They are not found in higher plants",
+    "57296f85af94a219006aa404": "roughly spherical",
+    "57296f85af94a219006aa405": "roughly spherical and highly refractive bodies",
+    "57296f85af94a219006aa406": "starch",
+    "57296f85af94a219006aa407": "divide to form new pyrenoids",
+    "57296fd71d0469140077943f": "helical thylakoid model",
+    "57296fd71d04691400779440": "circular",
+    "57296fd71d04691400779441": "two to a hundred",
+    "57296fd71d04691400779442": "two to a hundred",
+    "57296fd71d04691400779443": "helicoid stromal thylakoids",
+    "5729703d3f37b319004783bb": "light energy",
+    "5729703d3f37b319004783bc": "light energy",
+    "5729703d3f37b319004783bd": "energize electrons",
+    "5729703d3f37b319004783be": "pump hydrogen ions into the thylakoid space",
+    "5729703d3f37b319004783bf": "dam turbine",
+    "572970916aef051400154eba": "two",
+    "572970916aef051400154ebb": "pancake-shaped circular disks about 300\u2013600 nanometers in diameter",
+    "572970916aef051400154ebc": "in contact with the stroma",
+    "572970916aef051400154ebe": "pancake",
+    "572970916aef051400154ebd": "300\u2013600 nanometers in diameter",
+    "57297103af94a219006aa423": "thirty",
+    "57297103af94a219006aa424": "help transfer and dissipate excess energy",
+    "57297103af94a219006aa425": "help transfer and dissipate excess energy",
+    "57297103af94a219006aa426": "a bright red-orange carotenoid",
+    "57297103af94a219006aa427": "orange-red zeaxanthin",
+    "5729714daf94a219006aa42d": "a third group of pigments",
+    "5729714daf94a219006aa42e": "red algae red",
+    "5729714daf94a219006aa42f": "red algae red",
+    "5729714daf94a219006aa430": "relatively large protein complexes",
+    "5729714daf94a219006aa431": "40 nanometers across",
+    "572971af6aef051400154ede": "rubisco",
+    "572971af6aef051400154edf": "it has trouble distinguishing between carbon dioxide and oxygen",
+    "572971af6aef051400154ee0": "it has trouble distinguishing between carbon dioxide and oxygen",
+    "572971af6aef051400154ee1": "Calvin cycle",
+    "572971af6aef051400154ee2": "ATP energy",
+    "5729723c6aef051400154ee8": "light reactions",
+    "5729723c6aef051400154ee9": "rubisco",
+    "5729723c6aef051400154eea": "normal grana and thylakoids",
+    "5729723c6aef051400154eeb": "a four-carbon compound",
+    "5729723c6aef051400154eec": "photosynthesis",
+    "5729729a1d0469140077948b": "green",
+    "5729729a1d0469140077948c": "chloroplasts\u2014the chloroplasts",
+    "5729729a1d0469140077948d": "parenchyma cells",
+    "5729729a1d0469140077948e": "collenchyma tissue",
+    "5729729a1d0469140077948f": "A plant cell which contains chloroplasts",
+    "572972f46aef051400154ef2": "in the stems",
+    "572972f46aef051400154ef3": "leaves",
+    "572972f46aef051400154ef6": "8\u201315",
+    "572972f46aef051400154ef4": "half a million",
+    "572972f46aef051400154ef5": "mesophyll",
+    "5729735c3f37b319004783fb": "low-light conditions",
+    "5729735c3f37b319004783fc": "turning sideways",
+    "5729735c3f37b319004783fd": "photooxidative damage",
+    "5729735c3f37b319004783fe": "ability to distribute chloroplasts so that they can take shelter behind each other or spread out",
+    "5729735c3f37b319004783ff": "Mitochondria",
+    "572973ccaf94a219006aa449": "two",
+    "572973ccaf94a219006aa44a": "infected cells seal themselves off and undergo programmed cell death",
+    "572973ccaf94a219006aa44b": "where infected cells release signals warning the rest of the plant of a pathogen's presence",
+    "572973ccaf94a219006aa44c": "purposely damaging their photosynthetic system",
+    "572973ccaf94a219006aa44d": "reactive oxygen species",
+    "57297427af94a219006aa453": "salicylic acid, jasmonic acid, nitric oxide and reactive oxygen species",
+    "57297427af94a219006aa454": "After detecting stress in a cell",
+    "57297427af94a219006aa455": "pass on their signal to an unknown second messenger molecule",
+    "57297427af94a219006aa456": "signals from the chloroplast that regulate gene expression in the nucleus",
+    "572974923f37b3190047840b": "photosynthesis",
+    "572974923f37b3190047840c": "photosynthesis",
+    "572974923f37b3190047840d": "food",
+    "572974923f37b3190047840e": "Water (H2O) and carbon dioxide (CO2)",
+    "572974923f37b3190047840f": "chemical energy",
+    "572975073f37b31900478415": "use the potential energy stored in an H+, or hydrogen ion gradient to generate ATP energy",
+    "572975073f37b31900478416": "mitochondria",
+    "572975073f37b31900478417": "up to a thousand times as many",
+    "572975073f37b31900478418": "adenosine triphosphate",
+    "572975073f37b31900478419": "adenosine triphosphate",
+    "572975511d046914007794a7": "NADP+",
+    "572975511d046914007794a8": "cyclic photophosphorylation",
+    "572975511d046914007794a9": "C4 plants",
+    "572975511d046914007794aa": "more ATP than NADPH",
+    "572975a3af94a219006aa465": "Calvin cycle",
+    "572975a3af94a219006aa466": "unstable six-carbon molecules that immediately break down into three-carbon molecules",
+    "572975a3af94a219006aa467": "three-carbon molecules",
+    "572975a3af94a219006aa468": "one out of every six",
+    "572976183f37b31900478431": "glucose monomers",
+    "572976183f37b31900478432": "Under conditions such as high atmospheric CO2 concentrations",
+    "572976183f37b31900478433": "distorting the grana and thylakoids",
+    "572976183f37b31900478434": "Waterlogged",
+    "572976183f37b31900478435": "another photosynthesis-depressing factor",
+    "572976791d046914007794af": "add O2 instead of CO2 to RuBP",
+    "572976791d046914007794b0": "when the oxygen concentration is too high",
+    "572976791d046914007794b1": "reduces the efficiency of photosynthesis",
+    "572976791d046914007794b2": "up to half",
+    "572976791d046914007794b3": "chloroplast dimorphism",
+    "572976cfaf94a219006aa493": "stroma",
+    "572976cfaf94a219006aa494": "cysteine and methionine",
+    "572976cfaf94a219006aa495": "it has trouble crossing membranes to get to where it is needed",
+    "572976cfaf94a219006aa496": "the organelle carries out the last leg of the pathway or if it happens in the cytosol",
+    "57297725af94a219006aa49b": "Chloroplasts",
+    "57297725af94a219006aa49c": "undifferentiated proplastids",
+    "57297725af94a219006aa49d": "in an adult plant's apical meristems",
+    "57297725af94a219006aa49e": "starch-storing amyloplasts",
+    "5729779b6aef051400154f62": "proplastids may develop into an etioplast stage",
+    "5729779b6aef051400154f63": "a plastid",
+    "5729779b6aef051400154f64": "invaginations",
+    "5729779b6aef051400154f65": "yellow chlorophyll precursor",
+    "5729779b6aef051400154f66": "Gymnosperms",
+    "572977fbaf94a219006aa4ad": "proplastids",
+    "572977fbaf94a219006aa4ae": "pigment-filled plastids",
+    "572977fbaf94a219006aa4af": "chloroplasts",
+    "572977fbaf94a219006aa4b0": "chloroplasts",
+    "572977fbaf94a219006aa4b1": "chloroplasts",
+    "5729784b1d046914007794c9": "filaments",
+    "5729784b1d046914007794ca": "filaments",
+    "5729784b1d046914007794cb": "Z-ring within the chloroplast's stroma",
+    "5729784b1d046914007794cc": "chloroplast's stroma",
+    "5729784b1d046914007794cd": "The Min system",
+    "5729789b6aef051400154f6c": "two plastid-dividing rings",
+    "5729789b6aef051400154f6d": "two",
+    "5729789b6aef051400154f6e": "5 nanometers across",
+    "5729789b6aef051400154f6f": "5 nanometers across",
+    "5729789b6aef051400154f70": "chloroplasts have a third plastid-dividing ring located in the chloroplast's intermembrane space",
+    "572978e66aef051400154f76": "Light",
+    "572978e66aef051400154f78": "bright white light",
+    "572978e66aef051400154f79": "dumbbell-shaped",
+    "572978e66aef051400154f77": "bright white",
+    "57297991af94a219006aa4b7": "chloroplasts are not inherited from the male parent",
+    "57297991af94a219006aa4b8": "environmental risks",
+    "57297991af94a219006aa4b9": "3 in 1,000,000",
+    "57297991af94a219006aa4ba": "genetically modified plants that are biologically contained",
+    "57296d571d04691400779413": "1",
+    "57296d571d04691400779414": "composite number",
+    "57296d571d04691400779415": "The fundamental theorem of arithmetic",
+    "57296d571d04691400779416": "primes",
+    "57296d571d04691400779417": "one can include arbitrarily many instances of 1 in any factorization",
+    "57296f293f37b319004783a3": "primality",
+    "57296f293f37b319004783a4": "trial division",
+    "57296f293f37b319004783a5": "Miller\u2013Rabin primality test",
+    "57296f293f37b319004783a6": "Miller\u2013Rabin primality test",
+    "57296f293f37b319004783a7": "22,338,618",
+    "572970c11d04691400779463": "infinitely many",
+    "572970c11d04691400779464": "Euclid",
+    "572970c11d04691400779465": "statistical",
+    "572970c11d04691400779466": "prime number theorem",
+    "572970c11d04691400779467": "end of the 19th century",
+    "5729727baf94a219006aa437": "Goldbach's conjecture",
+    "5729727baf94a219006aa438": "twin prime conjecture",
+    "5729727baf94a219006aa439": "algebraic",
+    "5729727baf94a219006aa43a": "public-key cryptography",
+    "5729727baf94a219006aa43b": "twin prime conjecture",
+    "572973f76aef051400154f0a": "2",
+    "572973f76aef051400154f0b": "1, 2, and n",
+    "572973f76aef051400154f0c": "odd prime",
+    "572973f76aef051400154f0d": "9",
+    "572973f76aef051400154f0e": "even",
+    "57297547af94a219006aa45b": "1",
+    "57297547af94a219006aa45c": "Christian Goldbach",
+    "57297547af94a219006aa45d": "Christian Goldbach",
+    "57297547af94a219006aa45e": "10,006,721",
+    "57297547af94a219006aa45f": "prime",
+    "57297781af94a219006aa4a3": "Euclid's fundamental theorem of arithmetic",
+    "57297781af94a219006aa4a4": "1 were considered a prime",
+    "57297781af94a219006aa4a6": "the sum of divisors function",
+    "57297781af94a219006aa4a5": "sum of divisors function",
+    "57297781af94a219006aa4a7": "eliminate all multiples of 1",
+    "572978f91d046914007794d3": "Egyptian fraction expansions in the Rhind papyrus",
+    "572978f91d046914007794d4": "Ancient Greeks",
+    "572978f91d046914007794d5": "Euclid's Elements",
+    "572978f91d046914007794d6": "Euclid",
+    "572978f91d046914007794d7": "a simple method to compute primes",
+    "57297a276aef051400154f88": "1640",
+    "57297a276aef051400154f89": "Euler",
+    "57297a276aef051400154f8a": "22n + 1 are prime",
+    "57297a276aef051400154f8c": "2p \u2212 1",
+    "57297a276aef051400154f8b": "up to n = 4",
+    "57297bc9af94a219006aa4c7": "trial division",
+    "57297bc9af94a219006aa4c8": "a complete list of primes up to  is known",
+    "57297bc9af94a219006aa4c9": "1",
+    "57297bc9af94a219006aa4cb": "three",
+    "57297bc9af94a219006aa4ca": "the square root of n",
+    "57297d421d046914007794e5": "two",
+    "57297d421d046914007794e6": "probabilistic (or \"Monte Carlo\") and deterministic algorithms",
+    "57297d421d046914007794e7": "probabilistic (or \"Monte Carlo\") and deterministic algorithms",
+    "57297d421d046914007794e8": "deterministic",
+    "57297d421d046914007794e9": "1/(1-p)n",
+    "57297ed93f37b3190047845f": "Fermat primality test",
+    "57297ed93f37b31900478460": "the fact (Fermat's little theorem) that np\u2261n (mod p) for any n if p is a prime number",
+    "57297ed93f37b31900478461": "composite numbers",
+    "57297ed93f37b31900478462": "Baillie-PSW, Miller-Rabin, and Solovay-Strassen tests",
+    "57297ed93f37b31900478463": "Baillie-PSW, Miller-Rabin, and Solovay-Strassen tests",
+    "572980f9af94a219006aa4d1": "2p + 1 with p prime",
+    "572980f9af94a219006aa4d2": "factorial primes",
+    "572980f9af94a219006aa4d3": "Lucas\u2013Lehmer test",
+    "572980f9af94a219006aa4d4": "Sophie Germain primes",
+    "572980f9af94a219006aa4d5": "Sophie Germain primes",
+    "572982e66aef051400154f92": "distributed computing",
+    "572982e66aef051400154f93": "2009",
+    "572982e66aef051400154f94": "US$100,000",
+    "572982e66aef051400154f95": "Electronic Frontier Foundation",
+    "572982e76aef051400154f96": "256kn + 1, 256k(n + 1) \u2212 1",
+    "572985011d04691400779501": "floor function",
+    "572985011d04691400779502": "Chebyshev",
+    "572985011d04691400779503": "n > 3",
+    "572985011d04691400779504": "states that there always exists at least one prime number p with n < p < 2n \u2212 2",
+    "572985011d04691400779505": "Wilson's theorem",
+    "572987e46aef051400154fa2": "their greatest common divisor is one. If this necessary condition is satisfied, Dirichlet's theorem on arithmetic progressions",
+    "572987e46aef051400154fa3": "Dirichlet's theorem",
+    "572987e46aef051400154fa4": "1/6",
+    "572987e46aef051400154fa5": "one prime number",
+    "572987e46aef051400154fa6": "infinitely many prime numbers",
+    "572989846aef051400154fc0": "zeta function",
+    "572989846aef051400154fc1": "\u03b6(1) would have a finite value",
+    "572989846aef051400154fc2": "diverges",
+    "572989846aef051400154fc3": "exceeds any given number",
+    "572989846aef051400154fc4": "algebraic number theory is the following identity",
+    "57298ef11d0469140077952d": "1859",
+    "57298ef11d0469140077952e": "s = \u22122, \u22124",
+    "57298ef11d0469140077952f": "random noise",
+    "57298ef11d04691400779530": "asymptotic distribution of primes",
+    "57298ef11d04691400779531": "asymptotic distribution of primes",
+    "57299021af94a219006aa50c": "Goldbach's conjecture",
+    "57299021af94a219006aa50b": "1912",
+    "57299021af94a219006aa50d": "n = 2 \u00b7 1017",
+    "57299021af94a219006aa50e": "Vinogradov's theorem says that every sufficiently large odd integer can be written as a sum of three primes. Chen's theorem",
+    "57299021af94a219006aa50f": "Chen's theorem",
+    "572991943f37b319004784a1": "twin prime conjecture",
+    "572991943f37b319004784a2": "pairs of primes with difference 2",
+    "572991943f37b319004784a3": "Polignac's conjecture",
+    "572991943f37b319004784a4": "n2 + 1",
+    "572991943f37b319004784a5": "H. Brocard's conjecture",
+    "57299326af94a219006aa515": "number theory",
+    "57299326af94a219006aa516": "G. H. Hardy",
+    "57299326af94a219006aa517": "1970s",
+    "57299326af94a219006aa518": "hash tables and pseudorandom number generators",
+    "57299326af94a219006aa519": "pseudorandom",
+    "572995d46aef051400154fe8": "recurring",
+    "572995d46aef051400154fe9": "p \u2212 1 or a divisor of p \u2212 1",
+    "572995d46aef051400154fea": "(p \u2212 1)! + 1",
+    "572995d46aef051400154feb": "n \u2212 1)",
+    "572995d46aef051400154fec": "p is not a prime factor of q",
+    "572996c73f37b319004784b3": "large prime numbers",
+    "572996c73f37b319004784b4": "Diffie\u2013Hellman key exchange",
+    "572996c73f37b319004784b5": "512",
+    "572996c73f37b319004784b6": "modular",
+    "572996c73f37b319004784b7": "1024",
+    "572998673f37b319004784d5": "cicadas",
+    "572998673f37b319004784d6": "as grubs underground",
+    "572998673f37b319004784d7": "17 years",
+    "572998673f37b319004784d8": "the prime number intervals between emergences make it very difficult for predators to evolve",
+    "572998673f37b319004784d9": "2% higher",
+    "57299a6f6aef051400155016": "minimality or indecomposability",
+    "57299a6f6aef051400155017": "the prime field",
+    "57299a6f6aef051400155019": "a connected sum of prime knots",
+    "57299a6f6aef05140015501a": "any object can be, essentially uniquely, decomposed into its prime components",
+    "57299a6f6aef051400155018": "it cannot be written as the knot sum of two nontrivial knots",
+    "57299c2c6aef051400155020": "commutative ring R",
+    "57299c2c6aef051400155021": "Prime numbers",
+    "57299c2c6aef051400155022": "Prime numbers",
+    "57299c2c6aef051400155023": "neither zero nor a unit",
+    "57299c2c6aef051400155024": "if it is not a unit",
+    "57299d1c1d04691400779581": "The fundamental theorem of arithmetic",
+    "57299d1c1d04691400779582": "Gaussian integers",
+    "57299d1c1d04691400779583": "Z[i], that is, the set of complex numbers of the form a + bi",
+    "57299d1c1d04691400779584": "arbitrary integers",
+    "57299d1c1d04691400779585": "Gaussian primes, whereas rational primes of the form 4k + 1",
+    "57299ec43f37b3190047850d": "ring theory",
+    "57299ec43f37b3190047850e": "Prime ideals",
+    "57299ec43f37b3190047850f": "algebraic",
+    "57299ec43f37b31900478510": "fundamental theorem of arithmetic",
+    "57299ec43f37b31900478511": "Noetherian",
+    "5729a03f1d04691400779593": "Prime ideals",
+    "5729a03f1d04691400779594": "ramification of prime ideals when lifted to an extension field, a basic problem of algebraic number theory, bears some resemblance with ramification in geometry",
+    "5729a03f1d04691400779595": "ring of integers of quadratic number fields",
+    "5729a03f1d04691400779596": "solvability of quadratic equations",
+    "5729a26d6aef05140015505a": "gets smaller",
+    "5729a26d6aef05140015505b": "completed (or local) fields",
+    "5729a26d6aef05140015505c": "p-adic norm |\u2212|p",
+    "5729a26d6aef05140015505d": "local-global principle",
+    "5729a3716aef05140015506a": "Olivier Messiaen",
+    "5729a3716aef05140015506b": "La Nativit\u00e9 du Seigneur (1935) and Quatre \u00e9tudes de rythme",
+    "5729a3716aef05140015506c": "La Nativit\u00e9 du Seigneur (1935) and Quatre \u00e9tudes de rythme",
+    "5729a3716aef05140015506d": "Neumes rythmiques",
+    "5729a3716aef05140015506e": "the movements of nature, movements of free and unequal durations",
+    "572f5533a23a5019007fc55b": "Swiss canton of Graub\u00fcnden",
+    "572f5533a23a5019007fc55c": "North Sea in the Netherlands",
+    "572f5533a23a5019007fc55d": "Cologne",
+    "572f5533a23a5019007fc55e": "Danube",
+    "572f5533a23a5019007fc55f": "1,230 km (760 mi)",
+    "572fe1d404bcaa1900d76e37": "Swiss canton of Graub\u00fcnden in the southeastern Swiss Alps",
+    "572fe1d404bcaa1900d76e3a": "Netherlands",
+    "572fe1d404bcaa1900d76e3b": "1,230 km (760 mi)",
+    "572f55e8a23a5019007fc56b": "Gaulish name R\u0113nos",
+    "572f55e8a23a5019007fc56c": "Rhin",
+    "572f55e8a23a5019007fc56d": "*R\u012bnaz",
+    "572f55e8a23a5019007fc56e": "1st century BC",
+    "572fe288a23a5019007fcad7": "Gaulish name R\u0113nos",
+    "572fe288a23a5019007fcad8": "Rhin",
+    "572fe288a23a5019007fcada": "Rijn",
+    "572fe288a23a5019007fcad9": "*R\u012bnaz",
+    "572fe288a23a5019007fcadb": "Rhijn",
+    "572f567cb2c2fd140056803f": "Rhine-kilometers",
+    "572f567cb2c2fd1400568040": "1939",
+    "572f567cb2c2fd1400568041": "Old Rhine Bridge at Constance",
+    "572f567cb2c2fd1400568042": "Hoek van Holland",
+    "572f567cb2c2fd1400568043": "canalisation projects",
+    "572fe393947a6a140053cdba": "Rhine-kilometers",
+    "572fe393947a6a140053cdbb": "1939",
+    "572fe393947a6a140053cdbc": "Old Rhine Bridge at Constance",
+    "572fe393947a6a140053cdbe": "number of canalisation projects completed in the 19th and 20th century",
+    "572fe393947a6a140053cdbd": "Hoek van Holland",
+    "572f5703a23a5019007fc573": "north",
+    "572f5703a23a5019007fc574": "86 km",
+    "572f5703a23a5019007fc575": "Rhine Valley",
+    "572f5703a23a5019007fc576": "Sargans",
+    "572f5703a23a5019007fc577": "Liechtenstein",
+    "572fe41e04bcaa1900d76e4b": "near Chur",
+    "572fe41e04bcaa1900d76e4c": "86 km",
+    "572fe41e04bcaa1900d76e4d": "599 m to 396 m",
+    "572fe41e04bcaa1900d76e4e": "Rhine Valley",
+    "572fe41e04bcaa1900d76e4f": "Liechtenstein",
+    "572f57c704bcaa1900d7686d": "Lake Constance",
+    "572f57c704bcaa1900d7686e": "Alter Rhein",
+    "572f57c704bcaa1900d7686f": "a modern canalized section",
+    "572f57c704bcaa1900d76870": "Isel",
+    "572f57c704bcaa1900d76871": "Donkey",
+    "572fe4a304bcaa1900d76e55": "Lake Constance",
+    "572fe4a304bcaa1900d76e56": "a modern canalized section",
+    "572fe4a304bcaa1900d76e57": "Alter Rhein",
+    "572fe4a304bcaa1900d76e58": "small islands",
+    "572fe4a304bcaa1900d76e59": "Isel",
+    "572f5875947a6a140053c89a": "Diepoldsau",
+    "572f5875947a6a140053c89b": "an upper canal near Diepoldsau and a lower canal at Fu\u00dfach",
+    "572f5875947a6a140053c89c": "strong sedimentation",
+    "572f5875947a6a140053c89d": "parallel to the canalized Rhine into the lake",
+    "572f5875947a6a140053c89e": "silt up the lake",
+    "572fe53104bcaa1900d76e69": "Fu\u00dfach",
+    "572fe53104bcaa1900d76e6b": "to counteract the constant flooding and strong sedimentation in the western Rhine Delta",
+    "572fe53104bcaa1900d76e6a": "near Diepoldsau",
+    "572fe53104bcaa1900d76e6c": "Dornbirner Ach",
+    "572fe53104bcaa1900d76e6d": "continuous input of sediment",
+    "572f58d9a23a5019007fc57d": "three",
+    "572f58d9a23a5019007fc580": "lower lake",
+    "572f58d9a23a5019007fc57e": "Seerhein",
+    "572f58d9a23a5019007fc581": "Swiss-Austrian",
+    "572f58d9a23a5019007fc57f": "upper lake",
+    "572fe60fb2c2fd140056858b": "three",
+    "572fe60fb2c2fd140056858c": "Austria",
+    "572fe60fb2c2fd140056858d": "Alps",
+    "572fe60fb2c2fd140056858e": "near the Alps",
+    "572fe60fb2c2fd140056858f": "Baden-W\u00fcrttemberg",
+    "572f59b4a23a5019007fc587": "greater density of cold water",
+    "572f59b4a23a5019007fc58a": "Lake \u00dcberlingen",
+    "572f59b4a23a5019007fc588": "Upper Lake",
+    "572f59b4a23a5019007fc58b": "northern",
+    "572f59b4a23a5019007fc589": "Lindau",
+    "572fe92204bcaa1900d76e95": "Upper Lake",
+    "572fe92204bcaa1900d76e96": "Lindau",
+    "572fe92204bcaa1900d76e97": "Lake \u00dcberlingen",
+    "572fe92204bcaa1900d76e98": "Rhine Gutter",
+    "572fe92204bcaa1900d76e99": "water level",
+    "572f609ca23a5019007fc5af": "westward",
+    "572f609ca23a5019007fc5b0": "river Aare",
+    "572f609ca23a5019007fc5b1": "1,000 m3/s",
+    "572f609ca23a5019007fc5b2": "Finsteraarhorn",
+    "572f609ca23a5019007fc5b3": "Basel",
+    "572fe9b3947a6a140053cde0": "westward",
+    "572fe9b3947a6a140053cde1": "river Aare",
+    "572fe9b3947a6a140053cde2": "1,000 m3/s",
+    "572fe9b3947a6a140053cde3": "Finsteraarhorn",
+    "572fe9b3947a6a140053cde4": "German",
+    "572f60f4947a6a140053c8ea": "Basel",
+    "572f60f4947a6a140053c8eb": "Rhine knee",
+    "572f60f4947a6a140053c8ec": "the Central Bridge",
+    "572f60f4947a6a140053c8ed": "300 km",
+    "572f60f4947a6a140053c8ee": "40 km",
+    "572fec30947a6a140053cdf2": "Basel",
+    "572fec30947a6a140053cdf3": "Rhine knee",
+    "572fec30947a6a140053cdf4": "North",
+    "572fec30947a6a140053cdf5": "the High Rhine",
+    "572fec30947a6a140053cdf6": "the Central Bridge",
+    "572f64ccb2c2fd14005680b7": "19th",
+    "572f64ccb2c2fd14005680b8": "increased",
+    "572f64ccb2c2fd14005680b9": "fell",
+    "572f64ccb2c2fd14005680ba": "Grand Canal d'Alsace",
+    "572f64ccb2c2fd14005680bb": "large compensation pools",
+    "572feddda23a5019007fcb5b": "Upper Rhine region",
+    "572feddda23a5019007fcb5c": "19th Century",
+    "572feddda23a5019007fcb5d": "increased",
+    "572feddda23a5019007fcb5e": "fell significantly",
+    "572feddda23a5019007fcb5f": "Grand Canal d'Alsace",
+    "572f65e9b2c2fd14005680cc": "the Main and, later, the Moselle",
+    "572f65e9b2c2fd14005680cd": "300 m3/s",
+    "572f65e9b2c2fd14005680cb": "The Rhine",
+    "572f65e9b2c2fd14005680ce": "Moselle",
+    "572f65e9b2c2fd14005680cf": "400 m (1,300 ft)",
+    "572ff07304bcaa1900d76ef5": "Germany",
+    "572ff07304bcaa1900d76ef6": "Germany",
+    "572ff07304bcaa1900d76ef7": "Moselle",
+    "572ff07304bcaa1900d76ef8": "France",
+    "572ff07304bcaa1900d76ef9": "2,290 m3/s",
+    "572f6a0ba23a5019007fc5eb": "Middle Rhine",
+    "572f6a0ba23a5019007fc5ec": "Rhine Gorge",
+    "572f6a0ba23a5019007fc5ed": "erosion",
+    "572f6a0ba23a5019007fc5ee": "the Romantic Rhine",
+    "572ff12e04bcaa1900d76eff": "Middle Rhine",
+    "572ff12e04bcaa1900d76f00": "the Rhine Gorge",
+    "572ff12e04bcaa1900d76f02": "many castles and vineyards",
+    "572ff12e04bcaa1900d76f03": "the Romantic Rhine",
+    "572f6c85947a6a140053c940": "industry",
+    "572f6c85947a6a140053c942": "Duisburg",
+    "572f6c85947a6a140053c943": "The Ruhr",
+    "572f6c85947a6a140053c944": "drinking water",
+    "572f6c85947a6a140053c941": "along the Lower Rhine",
+    "572ff293947a6a140053ce52": "water pollution",
+    "572ff293947a6a140053ce53": "Lower Rhine",
+    "572ff293947a6a140053ce54": "Switzerland",
+    "572ff293947a6a140053ce55": "Duisburg",
+    "572ff293947a6a140053ce56": "The Ruhr",
+    "572f6ec7a23a5019007fc621": "tourism",
+    "572f6ec7a23a5019007fc622": "R\u00fcdesheim am Rhein",
+    "572f6ec7a23a5019007fc623": "Lorelei",
+    "572f6ec7a23a5019007fc624": "the Middle Rhine Valley",
+    "572ff35f947a6a140053ce66": "tourism",
+    "572ff35f947a6a140053ce67": "UNESCO World Heritage Site",
+    "572ff35f947a6a140053ce68": "R\u00fcdesheim am Rhein",
+    "572ff35f947a6a140053ce69": "Lorelei",
+    "572ff35f947a6a140053ce6a": "Near Sankt Goarshausen",
+    "572f7588947a6a140053c984": "Duisburg",
+    "572f7588947a6a140053c985": "Wesel-Datteln Canal",
+    "572f7588947a6a140053c986": "Lippe",
+    "572f7588947a6a140053c987": "Emmerich Rhine Bridge",
+    "572f7588947a6a140053c988": "400 m",
+    "572ff430a23a5019007fcba9": "Lower Rhine",
+    "572ff430a23a5019007fcbaa": "Rhine-Ruhr region",
+    "572ff430a23a5019007fcbab": "Duisport",
+    "572ff430a23a5019007fcbac": "Emmerich Rhine Bridge",
+    "572ff430a23a5019007fcbad": "400 m",
+    "572f76d1b2c2fd1400568154": "the Meuse",
+    "572f76d1b2c2fd1400568153": "Rijn",
+    "572f76d1b2c2fd1400568155": "Two thirds",
+    "572ff4ca04bcaa1900d76f24": "west",
+    "572ff4ca04bcaa1900d76f25": "farther west, through the Waal",
+    "572ff4ca04bcaa1900d76f26": "the Meuse",
+    "572ff4ca04bcaa1900d76f27": "The Oude Maas",
+    "572f7b33947a6a140053c9a2": "Pannerdens Kanaal",
+    "572f7b33947a6a140053c9a3": "Nederrijn",
+    "572f7b33947a6a140053c9a4": "the Lek",
+    "572f7b33947a6a140053c9a5": "Noord River",
+    "572ff56304bcaa1900d76f2d": "Pannerdens Kanaal",
+    "572ff56304bcaa1900d76f2e": "IJssel and Nederrijn",
+    "572ff56304bcaa1900d76f2f": "one ninth",
+    "572ff56304bcaa1900d76f30": "the Lek",
+    "572ff56304bcaa1900d76f31": "Wijk bij Duurstede",
+    "572ff5fcb2c2fd1400568657": "Rijn",
+    "572ff5fcb2c2fd1400568658": "draining the surrounding land and polders",
+    "572ff5fcb2c2fd1400568659": "Kromme Rijn",
+    "572ff5fcb2c2fd140056865a": "Kromme Rijn",
+    "572ff5fcb2c2fd140056865b": "sluice",
+    "572ff673b2c2fd1400568669": "Rhine-Meuse Delta",
+    "572ff673b2c2fd140056866a": "Millingen aan de Rijn",
+    "572ff673b2c2fd140056866b": "Rhine Delta",
+    "572ff7ab04bcaa1900d76f51": "Nederrijn",
+    "572ff7ab04bcaa1900d76f52": "three",
+    "572ff7ab04bcaa1900d76f53": "Waal",
+    "572ff7ab04bcaa1900d76f55": "Old Meuse",
+    "572ff7ab04bcaa1900d76f54": "the Rip",
+    "572ff83ab2c2fd1400568681": "St. Elizabeth's flood",
+    "572ff83ab2c2fd1400568682": "1421",
+    "572ff83ab2c2fd1400568683": "just south of today's line Merwede-Oude Maas to the North Sea",
+    "572ff83ab2c2fd1400568685": "1421 to 1904",
+    "572ff83ab2c2fd1400568684": "an archipelago-like estuary",
+    "572ff890a23a5019007fcbce": "drainage channels",
+    "572ff890a23a5019007fcbcf": "construction of Delta Works",
+    "572ff890a23a5019007fcbcd": "dammed",
+    "572ff890a23a5019007fcbd0": "second half of the 20th Century",
+    "572ff935b2c2fd140056869b": "tidal",
+    "572ff935b2c2fd140056869c": "tidal currents",
+    "572ff935b2c2fd140056869d": "tear huge areas of land into the sea",
+    "572ff935b2c2fd140056869e": "Zaltbommel",
+    "572ffa79a23a5019007fcbe9": "Tethys sea",
+    "572ffa79a23a5019007fcbeb": "Jurassic",
+    "572ffa79a23a5019007fcbec": "Mediterranean geography",
+    "572ffa79a23a5019007fcbea": "Triassic",
+    "572ffa79a23a5019007fcbed": "Iberia",
+    "572ffb02b2c2fd14005686b7": "N\u2013S",
+    "572ffb02b2c2fd14005686b8": "Upper Rhine Graben",
+    "572ffb02b2c2fd14005686b9": "Miocene",
+    "572ffb02b2c2fd14005686ba": "Rhone and Danube",
+    "572ffc0f947a6a140053cef0": "stream capture",
+    "572ffc0f947a6a140053cef1": "Pliocene period",
+    "572ffc0f947a6a140053cef2": "the Vosges Mountains",
+    "572ffce5a23a5019007fcc15": "Ice Ages",
+    "572ffce5a23a5019007fcc16": "six",
+    "572ffce5a23a5019007fcc17": "120 m (390 ft)",
+    "572ffce5a23a5019007fcc18": "northwest",
+    "572ffce5a23a5019007fcc19": "offshore of Brest, France",
+    "572ffd75b2c2fd14005686e5": "~74,000",
+    "572ffd75b2c2fd14005686e6": "Pleistocene",
+    "572ffd75b2c2fd14005686e7": "west",
+    "572ffd75b2c2fd14005686e8": "120 m (390 ft)",
+    "572ffd75b2c2fd14005686e9": "The English Channel, the Irish Channel and most of the North Sea were dry land",
+    "572fff45947a6a140053cf26": "a glacier",
+    "572fff45947a6a140053cf27": "A tundra",
+    "572fff45947a6a140053cf28": "22,000\u201314,000 yr BP",
+    "572fff45947a6a140053cf29": "ice-sheets",
+    "572fff45947a6a140053cf2a": "loess",
+    "572fffb404bcaa1900d76fef": "22,000 years ago",
+    "572fffb404bcaa1900d76ff0": "thaw",
+    "572fffb404bcaa1900d76ff1": "the Rhine",
+    "572fffb404bcaa1900d76ff2": "13,000 BP",
+    "572fffb404bcaa1900d76ff3": "9000 BP",
+    "573003dd947a6a140053cf42": "7500 yr ago",
+    "573003dd947a6a140053cf43": "Rates of sea-level rise",
+    "573003dd947a6a140053cf44": "7000 years",
+    "573003dd947a6a140053cf45": "ongoing tectonic subsidence",
+    "573003dd947a6a140053cf46": "1\u20133 cm (0.39\u20131.18 in) per century",
+    "5730042804bcaa1900d77011": "~11,700 years ago",
+    "5730042804bcaa1900d77014": "~8,000 years ago",
+    "5730042804bcaa1900d77012": "Late-Glacial valley",
+    "5730042804bcaa1900d77013": "Netherlands",
+    "57300580b2c2fd140056874d": "~3000 yr BP",
+    "57300580b2c2fd140056874f": "flooding",
+    "57300580b2c2fd140056874e": "sediment load",
+    "57300580b2c2fd1400568751": "11\u201313th century AD",
+    "57300580b2c2fd1400568750": "80",
+    "5730069004bcaa1900d7702d": "North Sea",
+    "5730069004bcaa1900d7702e": "North Sea",
+    "5730069004bcaa1900d7702f": "to the north",
+    "5730069004bcaa1900d77030": "IJsselmeer",
+    "5730069004bcaa1900d77031": "three",
+    "57300761947a6a140053cf9a": "1st century BC",
+    "57300761947a6a140053cf9b": "Germania",
+    "57300761947a6a140053cf9c": "6th century BC",
+    "57300761947a6a140053cf9d": "Herodotus",
+    "573007fab2c2fd140056876b": "AD 14",
+    "573007fab2c2fd140056876c": "upper Danube",
+    "573007fab2c2fd140056876d": "until the empire fell",
+    "573007fab2c2fd140056876f": "eastwards",
+    "573007fab2c2fd140056876e": "The upper Rhine and upper Danube are easily crossed",
+    "57300888b2c2fd1400568775": "eight",
+    "57300888b2c2fd1400568777": "Pannonian troops",
+    "57300888b2c2fd1400568778": "oppidum Ubiorum",
+    "57300888b2c2fd1400568776": "whether a state or threat of war existed",
+    "57300888b2c2fd1400568779": "oppidum Ubiorum (\"town of the Ubii",
+    "57300911947a6a140053cfb6": "5th century",
+    "57300911947a6a140053cfb7": "kingdoms of Francia on the Lower Rhine, Burgundy on the Upper Rhine and Alemannia on the High Rhine",
+    "57300911947a6a140053cfb8": "dragons rock",
+    "57300911947a6a140053cfb9": "Siegfried",
+    "57300911947a6a140053cfba": "Hagen",
+    "573009a004bcaa1900d7704f": "6th century",
+    "573009a004bcaa1900d77050": "10th century",
+    "573009a004bcaa1900d77051": "Lower Lorraine",
+    "573009a004bcaa1900d77052": "Archduke Sigismund of Austria",
+    "573009a004bcaa1900d77053": "1469",
+    "57300a25a23a5019007fcc97": "the Peace of Westphalia",
+    "57300a25a23a5019007fcc98": "Establishing \"natural borders\" on the Rhine",
+    "57300a25a23a5019007fcc99": "Napoleon",
+    "57300a25a23a5019007fcc9a": "1814",
+    "57300a25a23a5019007fcc9b": "1840",
+    "57300a9a04bcaa1900d77063": "end of World War I",
+    "57300a9a04bcaa1900d77064": "1930",
+    "57300a9a04bcaa1900d77065": "German army",
+    "57300a9a04bcaa1900d77066": "Adolf Hitler's rise to power",
+    "57300a9a04bcaa1900d77067": "1936",
+    "57300bf504bcaa1900d77087": "Arnhem",
+    "57300bf504bcaa1900d77088": "a formidable natural obstacle",
+    "57300bf504bcaa1900d77089": "September 1944",
+    "57300bf504bcaa1900d7708a": "Ludendorff Bridge",
+    "57300bf504bcaa1900d7708b": "Seven Days to the River Rhine",
+    "57300c67947a6a140053cff0": "1,230 kilometres",
+    "57300c67947a6a140053cff1": "Knaurs Lexikon",
+    "57300c67947a6a140053cff3": "a typographical error",
+    "57300c67947a6a140053cff2": "1,230 kilometres (764 miles)",
+    "57300c68947a6a140053cff4": "2010",
+    "572fac17947a6a140053cb54": "1998",
+    "572fac17947a6a140053cb55": "Scotland Act 1998",
+    "572fac17947a6a140053cb56": "the areas in which it can make laws",
+    "572fac17947a6a140053cb57": "the Parliament of the United Kingdom",
+    "572fac17947a6a140053cb58": "Westminster",
+    "572facb0a23a5019007fc863": "the lack of a Parliament of Scotland",
+    "572facb0a23a5019007fc864": "three",
+    "572facb0a23a5019007fc865": "the outbreak of the First World War",
+    "572facb0a23a5019007fc866": "late 1960s",
+    "572facb0a23a5019007fc867": "a directly elected Scottish Assembly",
+    "572fad30a23a5019007fc86d": "North Sea",
+    "572fad30a23a5019007fc86e": "\"It's Scotland's oil\"",
+    "572fad30a23a5019007fc870": "1974",
+    "572fad30a23a5019007fc86f": "the revenues from the oil were not benefitting Scotland as much as they should",
+    "572fad30a23a5019007fc871": "1978",
+    "572fadcbb2c2fd1400568329": "Edinburgh",
+    "572fadcbb2c2fd140056832a": "40%",
+    "572fadcbb2c2fd140056832c": "51.6%",
+    "572fadcbb2c2fd140056832b": "failed",
+    "572fadcbb2c2fd140056832d": "32.9%",
+    "572fae4b04bcaa1900d76bdf": "Scottish Parliament",
+    "572fae4b04bcaa1900d76be0": "Conservative Party",
+    "572fae4b04bcaa1900d76be1": "1989",
+    "572fae4b04bcaa1900d76be2": "blueprint for devolution",
+    "572faec7b2c2fd1400568333": "a new Scottish Parliament Building, in the Holyrood area of Edinburgh",
+    "572faec7b2c2fd1400568334": "Enric Miralles",
+    "572faec7b2c2fd1400568335": "Spanish",
+    "572faec7b2c2fd1400568336": "leaf-shaped",
+    "572faec7b2c2fd1400568337": "Queen Elizabeth II",
+    "572faf74b2c2fd1400568349": "the meeting of the Church's General Assembly",
+    "572faf74b2c2fd1400568347": "the General Assembly Hall of the Church of Scotland on the Royal Mile in Edinburgh",
+    "572faf74b2c2fd1400568348": "General Assembly Hall of the Church of Scotland on the Royal Mile in Edinburgh. Official photographs and television interviews were held in the courtyard adjoining the Assembly Hall",
+    "572faf74b2c2fd140056834b": "University of Aberdeen",
+    "572faf74b2c2fd140056834a": "Strathclyde Regional Council debating chamber in Glasgow",
+    "572fb059947a6a140053cb80": "City of Edinburgh Council",
+    "572fb059947a6a140053cb81": "Lothian Regional Council on George IV Bridge",
+    "572fb059947a6a140053cb82": "demolished",
+    "572fb059947a6a140053cb83": "Parliament Square, High Street and George IV Bridge in Edinburgh",
+    "572fb059947a6a140053cb84": "the main hall",
+    "572fb42904bcaa1900d76c15": "one MSP",
+    "572fb42904bcaa1900d76c16": "Tricia Marwick",
+    "572fb42904bcaa1900d76c17": "secret",
+    "572fb42904bcaa1900d76c18": "129",
+    "572fb42904bcaa1900d76c19": "A vote clerk",
+    "572fbb04a23a5019007fc8f7": "the Presiding Officer",
+    "572fbb04a23a5019007fc8f8": "Parliamentary Bureau",
+    "572fbb04a23a5019007fc8f9": "five",
+    "572fbb04a23a5019007fc8fa": "The Presiding Officer",
+    "572fbea404bcaa1900d76c5b": "hemicycle",
+    "572fbea404bcaa1900d76c5c": "reflects the desire to encourage consensus amongst elected members",
+    "572fbea404bcaa1900d76c5d": "131",
+    "572fbea404bcaa1900d76c5e": "2",
+    "572fbea404bcaa1900d76c5f": "cannot vote",
+    "572fbf21a23a5019007fc938": "Scottish rivers",
+    "572fbf21a23a5019007fc937": "silver",
+    "572fbf21a23a5019007fc93a": "the Queen",
+    "572fbf21a23a5019007fc939": "Wisdom, Compassion, Justice and Integrity",
+    "572fbf21a23a5019007fc93b": "a glass case",
+    "572fbfa504bcaa1900d76c73": "April and October",
+    "572fbfa504bcaa1900d76c74": "debating chamber",
+    "572fbfa504bcaa1900d76c75": "the public",
+    "572fbfa504bcaa1900d76c76": "free",
+    "572fbfa504bcaa1900d76c77": "Official Report",
+    "572fc043a23a5019007fc95d": "Wednesdays",
+    "572fc043a23a5019007fc95e": "up to four minutes",
+    "572fc043a23a5019007fc960": "Presiding Officer",
+    "572fc043a23a5019007fc95f": "religious beliefs",
+    "572fc043a23a5019007fc961": "nominate speakers",
+    "572fc41f947a6a140053cc80": "The Presiding Officer",
+    "572fc41f947a6a140053cc81": "who speaks in chamber debates",
+    "572fc41f947a6a140053cc82": "different viewpoints and political parties",
+    "572fc41f947a6a140053cc83": "ministers or party leaders",
+    "572fc41f947a6a140053cc84": "Gaelic",
+    "572fc49d04bcaa1900d76ccb": "5 pm",
+    "572fc49d04bcaa1900d76ccc": "Decision Time",
+    "572fc49d04bcaa1900d76ccd": "vote",
+    "572fc49d04bcaa1900d76cce": "by means of electronic consoles on their desks",
+    "572fc49d04bcaa1900d76ccf": "in seconds",
+    "572fc5a1947a6a140053cc8a": "outcome of most votes",
+    "572fc5a1947a6a140053cc8b": "political parties",
+    "572fc5a1947a6a140053cc8c": "whips",
+    "572fc5a1947a6a140053cc8e": "moral",
+    "572fc5a1947a6a140053cc8d": "expelled from their parties outright",
+    "572fc659b2c2fd1400568447": "Immediately after Decision Time",
+    "572fc659b2c2fd1400568449": "Such motions are on issues which may be of interest to a particular area",
+    "572fc659b2c2fd1400568448": "45 minutes",
+    "572fc659b2c2fd140056844a": "other members",
+    "572fc659b2c2fd140056844b": "winds up\" the debate",
+    "572fc6f204bcaa1900d76cf5": "in committee",
+    "572fc6f204bcaa1900d76cf6": "stronger",
+    "572fc6f204bcaa1900d76cf7": "there is no revising chamber",
+    "572fc6f204bcaa1900d76cf8": "conduct inquiries and scrutinise legislation",
+    "572fc6f204bcaa1900d76cf9": "Tuesday, Wednesday and Thursday morning when Parliament is sitting. Committees can also meet at other locations throughout Scotland",
+    "572fc78d04bcaa1900d76d07": "MSPs",
+    "572fc78d04bcaa1900d76d08": "the balance of parties across Parliament",
+    "572fc78d04bcaa1900d76d09": "functions",
+    "572fc78d04bcaa1900d76d0a": "Mandatory Committees",
+    "572fc78d04bcaa1900d76d0b": "fourth",
+    "572fc80b04bcaa1900d76d11": "at the beginning of each parliamentary session",
+    "572fc80b04bcaa1900d76d12": "one",
+    "572fc80b04bcaa1900d76d13": "Subject Committees",
+    "572fc80b04bcaa1900d76d14": "Economy, Energy and Tourism",
+    "572fc8a904bcaa1900d76d1f": "A further type of committee",
+    "572fc8a904bcaa1900d76d20": "large-scale development projects",
+    "572fc8a904bcaa1900d76d21": "Private bills",
+    "572fc8a904bcaa1900d76d22": "Private Bill Committees",
+    "572fca7eb2c2fd1400568473": "The Scotland Act 1998",
+    "572fca7eb2c2fd1400568474": "Queen Elizabeth II",
+    "572fca7eb2c2fd1400568475": "devolved competencies",
+    "572fca7eb2c2fd1400568476": "the Parliament of the United Kingdom at Westminster",
+    "572fca7eb2c2fd1400568477": "the Parliament",
+    "572fcb6da23a5019007fc9f1": "Schedule 5",
+    "572fcb6da23a5019007fc9f2": "Scottish Parliament",
+    "572fcb6da23a5019007fc9f3": "All matters that are not specifically reserved",
+    "572fcb6da23a5019007fc9f4": "3 pence in the pound",
+    "572fcb6da23a5019007fc9f5": "2012",
+    "572fcc43b2c2fd140056847d": "Reserved matters",
+    "572fcc43b2c2fd140056847e": "The Scottish Parliament",
+    "572fcc43b2c2fd140056847f": "Westminster",
+    "572fcc43b2c2fd1400568480": "Scotland Parliament",
+    "572fcd86947a6a140053ccda": "Bills",
+    "572fcd86947a6a140053ccdb": "the Scottish Government",
+    "572fcd86947a6a140053ccdc": "a private member",
+    "572fcd86947a6a140053ccdd": "an outside proposer",
+    "572fcd86947a6a140053ccde": "in a number of stages",
+    "572fce12a23a5019007fca11": "the first, or introductory stage",
+    "572fce13a23a5019007fca12": "Statements from the Presiding Officer and the member in charge of the bill",
+    "572fce13a23a5019007fca13": "whether the bill is within the legislative competence of the Parliament",
+    "572fce13a23a5019007fca14": "relevant committee or committees",
+    "572fce13a23a5019007fca15": "Stage 2",
+    "572fd1c4947a6a140053cd02": "Stage 3",
+    "572fd1c4947a6a140053cd03": "two",
+    "572fd1c4947a6a140053cd04": "final vote",
+    "572fd1c4947a6a140053cd05": "wrecking amendments",
+    "572fd1c4947a6a140053cd06": "at Decision Time",
+    "572fd264b2c2fd14005684a9": "the Monarch",
+    "572fd264b2c2fd14005684aa": "the Presiding Officer submits it to the Monarch for royal assent",
+    "572fd264b2c2fd14005684ab": "4-week",
+    "572fd264b2c2fd14005684ac": "the Supreme Court of the United Kingdom",
+    "572fd264b2c2fd14005684ad": "a conventional enacting formula",
+    "572fd47fa23a5019007fca55": "The party, or parties, that hold the majority of seats in the Parliament",
+    "572fd47fa23a5019007fca57": "Any member",
+    "572fd47fa23a5019007fca56": "a First Minister",
+    "572fd47fa23a5019007fca58": "the Lord Advocate and the Solicitor General",
+    "572fd47fa23a5019007fca59": "the Sovereign",
+    "572fd6aa04bcaa1900d76d9f": "Thursday in May",
+    "572fd6aa04bcaa1900d76da0": "May",
+    "572fd6aa04bcaa1900d76da1": "Presiding Officer",
+    "572fd6aa04bcaa1900d76da3": "supplant it",
+    "572fd6aa04bcaa1900d76da2": "28",
+    "572fd73e947a6a140053cd32": "Several procedures",
+    "572fd73e947a6a140053cd33": "MSPs",
+    "572fd73e947a6a140053cd34": "a statement",
+    "572fd73e947a6a140053cd35": "issues related to the substance of the statement",
+    "572fd7b8947a6a140053cd3a": "Parliamentary time",
+    "572fd7b8947a6a140053cd3b": "Thursday",
+    "572fd7b8947a6a140053cd3c": "any member of the Scottish Government",
+    "572fd7b8947a6a140053cd3d": "issues under their jurisdiction",
+    "572fd7b8947a6a140053cd3e": "four",
+    "572fd8efb2c2fd14005684f9": "129",
+    "572fd8efb2c2fd14005684fb": "2005",
+    "572fd8efb2c2fd14005684fa": "one",
+    "572fd8efb2c2fd14005684fd": "dispersed population and distance from the Scottish Parliament in Edinburgh",
+    "572fd8efb2c2fd14005684fc": "55,000",
+    "572fda6fb2c2fd140056850b": "proportionally to the number of votes received in the second vote of the ballot using the d'Hondt method",
+    "572fda6fb2c2fd140056850c": "d'Hondt method",
+    "572fda6fb2c2fd140056850d": "quotient",
+    "572fda6fb2c2fd140056850e": "constituency seats",
+    "572fda6fb2c2fd140056850f": "iteratively",
+    "572fdb17b2c2fd140056851d": "a number of qualifications",
+    "572fdb17b2c2fd140056851e": "1981",
+    "572fdb17b2c2fd140056851f": "18",
+    "572fdb17b2c2fd1400568520": "police and the armed forces",
+    "572fdb17b2c2fd1400568521": "Mental Health (Care and Treatment) (Scotland) Act 2003",
+    "572fdbb004bcaa1900d76dd9": "a party has commanded a parliamentary majority",
+    "572fdbb004bcaa1900d76dda": "Labour",
+    "572fdbb004bcaa1900d76ddb": "151 votes",
+    "572fdbb004bcaa1900d76ddc": "eight",
+    "572fdbb004bcaa1900d76ddd": "Scottish independence",
+    "572fdc34a23a5019007fca93": "the Conservatives",
+    "572fdc34a23a5019007fca94": "Edinburgh Pentlands",
+    "572fdc34a23a5019007fca95": "five seats",
+    "572fdc34a23a5019007fca96": "Annabel Goldie",
+    "572fdc34a23a5019007fca97": "Cameron",
+    "572fdd03a23a5019007fca9d": "are able to vote on domestic legislation that applies only to England, Wales and Northern Ireland",
+    "572fdd03a23a5019007fca9e": "domestic legislation of the Scottish Parliament",
+    "572fdd03a23a5019007fca9f": "the West Lothian question",
+    "572fdd03a23a5019007fcaa0": "Conservative",
+    "572fdd03a23a5019007fcaa1": "England",
+    "572ff626947a6a140053ce8e": "Islamism",
+    "572ff626947a6a140053ce8f": "all spheres",
+    "572ff626947a6a140053ce90": "reordering of government and society in accordance with the Shari'a",
+    "572ff626947a6a140053ce91": "two poles",
+    "572ff626947a6a140053ce92": "revolution or invasion",
+    "572ff760b2c2fd1400568677": "democratic",
+    "572ff760b2c2fd1400568678": "Palestine",
+    "572ff760b2c2fd1400568679": "seeking to abolish the state of Israel",
+    "572ff760b2c2fd140056867a": "democracy",
+    "572ff760b2c2fd140056867b": "a religious",
+    "572ff86004bcaa1900d76f65": "division",
+    "572ff86004bcaa1900d76f66": "Sunni pan-Islamism",
+    "572ff86004bcaa1900d76f67": "sharia",
+    "572ff86004bcaa1900d76f68": "democracy",
+    "572ff86004bcaa1900d76f69": "to maintain their legitimacy",
+    "572ff932a23a5019007fcbd5": "political",
+    "572ff932a23a5019007fcbd6": "Islam",
+    "572ff932a23a5019007fcbd7": "progressive moderates",
+    "572ff932a23a5019007fcbd8": "illiberal Islamic regimes",
+    "572ff932a23a5019007fcbd9": "religion from politics",
+    "572ffabf04bcaa1900d76f9f": "Islamism",
+    "572ffabf04bcaa1900d76fa0": "Americans",
+    "572ffabf04bcaa1900d76fa1": "a historical fluke of the \"short-lived era of the heyday of secular Arab nationalism",
+    "572ffabf04bcaa1900d76fa2": "between 1945 and 1970",
+    "572ffabf04bcaa1900d76fa3": "quietist/non-political Islam",
+    "572ffbaa947a6a140053cee7": "dangerous enemies",
+    "572ffbaa947a6a140053cee6": "1970s",
+    "572ffbaa947a6a140053ceea": "experience, ideology, and weapons",
+    "572ffbaa947a6a140053cee9": "mujahideen Muslim Afghanistan enemies",
+    "572ffbaa947a6a140053cee8": "leftist/communist/nationalist insurgents/opposition",
+    "572ffbaab2c2fd14005686cd": "experience, ideology, and weapons",
+    "572ffc99947a6a140053cef6": "Anwar Sadat",
+    "572ffc99947a6a140053cef7": "making peace",
+    "572ffc99947a6a140053cef8": "making peace with Israel",
+    "572ffc99947a6a140053cef9": "1975",
+    "572ffc99947a6a140053cefa": "political support",
+    "572ffd9e04bcaa1900d76fc7": "Saudi-based Wahhabism",
+    "572ffd9e04bcaa1900d76fc8": "hate them for their religion",
+    "572ffd9e04bcaa1900d76fc9": "wars",
+    "572ffd9e04bcaa1900d76fca": "infidels",
+    "572ffd9e04bcaa1900d76fcb": "Saudi-interpretation",
+    "572ffe6fb2c2fd14005686ef": "Islamist",
+    "572ffe6fb2c2fd14005686f2": "incompetent, inefficient, or neglectful",
+    "572ffe6fb2c2fd14005686f0": "housing",
+    "572ffe6fb2c2fd14005686f3": "rhetoric",
+    "572ffe6fb2c2fd14005686f1": "to avoid prohibitively costly dowry demands, legal assistance, sports facilities, and women's groups",
+    "572ffee1947a6a140053cf14": "law and philosophy",
+    "572ffee1947a6a140053cf15": "All India Muslim League",
+    "572ffee1947a6a140053cf17": "Indian National Congress",
+    "572ffee1947a6a140053cf16": "1908",
+    "572ffee1947a6a140053cf18": "The Reconstruction of Religious Thought in Islam",
+    "572fffb1b2c2fd14005686f9": "secularism and secular nationalism",
+    "572fffb1b2c2fd14005686fa": "crowd",
+    "572fffb1b2c2fd14005686fb": "nationalist differences",
+    "572fffb1b2c2fd14005686fc": "1930",
+    "572fffb1b2c2fd14005686fd": "Pakistan movement",
+    "5730005db2c2fd1400568703": "Sayyid Abul Ala Maududi",
+    "5730005db2c2fd1400568704": "journalism",
+    "5730005db2c2fd1400568705": "1941",
+    "5730005db2c2fd1400568706": "through his writing than through his political organising",
+    "5730005db2c2fd1400568707": "modern context",
+    "57300137b2c2fd1400568717": "Sayyid Abul Ala Maududi",
+    "57300137b2c2fd1400568718": "journalism",
+    "57300137b2c2fd140056871a": "through his writing than through his political organising",
+    "57300137b2c2fd140056871b": "modern context",
+    "57300200b2c2fd1400568729": "Sharia",
+    "57300200b2c2fd140056872a": "an Islamic state",
+    "57300200b2c2fd140056872b": "unity of God",
+    "57300200b2c2fd140056872c": "Islamic",
+    "57300200b2c2fd140056872d": "educational process or da'wah",
+    "5730035e04bcaa1900d77001": "1928",
+    "5730035e04bcaa1900d77002": "Ismailiyah, Egypt",
+    "5730035e04bcaa1900d77003": "Hassan al Banna",
+    "5730035e04bcaa1900d77004": "the Qur'an",
+    "5730035e04bcaa1900d77005": "imperialist",
+    "5730040f947a6a140053cf4c": "violence",
+    "5730040f947a6a140053cf4d": "1949",
+    "5730040f947a6a140053cf4e": "Mahmud Fami Naqrashi",
+    "5730040f947a6a140053cf4f": "1948",
+    "5730040f947a6a140053cf50": "Gamal Abdul Nasser",
+    "573004bf947a6a140053cf56": "one of the most influential movements",
+    "573004bf947a6a140053cf59": "75%",
+    "573004bf947a6a140053cf57": "semi-legal",
+    "573004bf947a6a140053cf58": "field candidates",
+    "573004bf947a6a140053cf5a": "Mohamed Morsi",
+    "573005b9947a6a140053cf6a": "quick and decisive",
+    "573005b9947a6a140053cf6b": "a pivotal event",
+    "573005b9947a6a140053cf6c": "economic",
+    "573005b9947a6a140053cf6d": "decline",
+    "573005b9947a6a140053cf6e": "different democratic and anti-democratic Islamist movements",
+    "5730088e947a6a140053cfad": "ideological",
+    "5730088e947a6a140053cfac": "Ali Shariati",
+    "5730088e947a6a140053cfae": "somewhere between",
+    "5730088e947a6a140053cfaf": "Prophet Mohammad",
+    "5730088e947a6a140053cfb0": "conspiracy",
+    "57300e2604bcaa1900d770b7": "The Islamic Republic",
+    "57300e2604bcaa1900d770b9": "Shia terrorist groups",
+    "57300e2604bcaa1900d770b8": "economic",
+    "57300e2604bcaa1900d770ba": "2006 Israel-Lebanon conflict",
+    "57300e2604bcaa1900d770bb": "President Mahmoud Ahmadinejad",
+    "57300ec0947a6a140053d004": "the Soviet Union",
+    "57300ec0947a6a140053d005": "an Islamic rebellion",
+    "57300ec0947a6a140053d006": "send aid",
+    "57300ec0947a6a140053d007": "marginal",
+    "57300ec0947a6a140053d008": "16,000 to 35,000",
+    "57300f8504bcaa1900d770d1": "put an end to Saddam Hussein's occupation of Kuwait",
+    "57300f8504bcaa1900d770d2": "Saddam Hussein",
+    "57300f8504bcaa1900d770d3": "Islamist",
+    "57300f8504bcaa1900d770d4": "Saudi",
+    "57300f8504bcaa1900d770d5": "the west",
+    "573010fab2c2fd14005687d7": "conservative Muslims",
+    "573010fab2c2fd14005687d9": "domestic Islamists",
+    "573010fab2c2fd14005687d8": "the kingdom",
+    "573010fab2c2fd14005687da": "Algeria",
+    "573010fab2c2fd14005687db": "Osama bin Laden",
+    "573011de04bcaa1900d770f9": "Qutb",
+    "573011de04bcaa1900d770fa": "1966",
+    "573011de04bcaa1900d770fb": "the Brotherhood",
+    "573011de04bcaa1900d770fc": "manifesto Milestones",
+    "573011de04bcaa1900d770fd": "1970s",
+    "5730126ba23a5019007fcd09": "Egyptian Islamic Jihad organization",
+    "5730126ba23a5019007fcd0a": "1981",
+    "5730126ba23a5019007fcd0b": "\"apostate\" leaders of Muslim states",
+    "5730126ba23a5019007fcd0c": "apostate",
+    "5730126ba23a5019007fcd0d": "Muhammad Abd al-Salaam Farag",
+    "5730131c947a6a140053d052": "violence",
+    "5730131c947a6a140053d053": "counter-terrorism police",
+    "5730131c947a6a140053d055": "2003",
+    "5730131c947a6a140053d054": "unsuccessful",
+    "5730131c947a6a140053d056": "political figures",
+    "5730208fa23a5019007fcded": "quiescent",
+    "5730208fa23a5019007fcdee": "HAMAS",
+    "5730208fa23a5019007fcdef": "the destruction of Israel and the establishment of an Islamic state in Palestine",
+    "5730208fa23a5019007fcdf1": "alcohol",
+    "5730208fa23a5019007fcdf0": "Palestine",
+    "573020f7b2c2fd14005688f7": "Hamas",
+    "573020f7b2c2fd14005688f8": "542",
+    "573020f7b2c2fd14005688f9": "the majority of the seats",
+    "573020f7b2c2fd14005688fa": "2007",
+    "573020f7b2c2fd14005688fb": "driving Israel out of the Gaza Strip",
+    "57302700a23a5019007fce89": "Islamist",
+    "57302700a23a5019007fce8a": "Hassan al-Turabi",
+    "57302700a23a5019007fce8b": "National Islamic Front",
+    "57302700a23a5019007fce8c": "with money from foreign Islamist banking systems",
+    "57302700a23a5019007fce8d": "university and military academy",
+    "573027d6a23a5019007fce9d": "1985",
+    "573027d6a23a5019007fce9e": "with the help of the military",
+    "573027d6a23a5019007fce9f": "sharia law",
+    "573027d6a23a5019007fcea0": "Osama bin Laden",
+    "573027d6a23a5019007fcea1": "American attack on Iraq",
+    "5730285a04bcaa1900d77270": "unemployment",
+    "5730285a04bcaa1900d7726f": "1989",
+    "5730285a04bcaa1900d7726e": "Algeria",
+    "5730285a04bcaa1900d7726d": "Front Islamique de Salut",
+    "5730285a04bcaa1900d77271": "a military coup d'\u00e9tat",
+    "573028fa04bcaa1900d77287": "justice and prosperity",
+    "573028fa04bcaa1900d77288": "vicious and destructive",
+    "573028fa04bcaa1900d7728a": "1992",
+    "573028fa04bcaa1900d77289": "one of the poorest countries on earth",
+    "573028fa04bcaa1900d7728b": "80%",
+    "57302a3aa23a5019007fcecf": "The Taliban",
+    "57302a3aa23a5019007fced0": "Pakistan",
+    "57302a3aa23a5019007fced1": "Islamic fundamentalist or neofundamentalist",
+    "57302a3aa23a5019007fced2": "Sharia",
+    "57302a3aa23a5019007fced3": "Wahhabism, and the extremist jihadism of their guest Osama bin Laden",
+    "57302ad804bcaa1900d772af": "July 1977",
+    "57302ad804bcaa1900d772b0": "alcohol and nightclubs",
+    "57302ad804bcaa1900d772b1": "Islamism",
+    "57302ad804bcaa1900d772b2": "his means of seizing power",
+    "57302ad804bcaa1900d772b3": "1988",
+    "57302bd0b2c2fd14005689db": "Wahhabi/Salafi jihadist extremist",
+    "57302bd0b2c2fd14005689dc": "Sunni Arabs",
+    "57302bd0b2c2fd14005689de": "ten million",
+    "57302bd0b2c2fd14005689df": "international recognition",
+    "57302bd0b2c2fd14005689dd": "caliphate",
+    "57302cd004bcaa1900d772d7": "2004",
+    "57302cd004bcaa1900d772d8": "March 2003",
+    "57302cd004bcaa1900d772da": "failure to consult and \"notorious intransigence\"",
+    "57302cd004bcaa1900d772d9": "March 2011",
+    "57302cd004bcaa1900d772db": "terrorist organisation",
+    "57302e45947a6a140053d22e": "different view",
+    "57302e45947a6a140053d22f": "1924",
+    "57302e45947a6a140053d230": "1924",
+    "57302e45947a6a140053d231": "true Islamic system",
+    "57302e45947a6a140053d232": "ended the true Islamic system",
+    "57302efe04bcaa1900d772f5": "armed",
+    "57302efe04bcaa1900d772f6": "ideological struggle",
+    "57302efe04bcaa1900d772f7": "Muslim",
+    "57302efe04bcaa1900d772f8": "Egypt",
+    "57302efe04bcaa1900d772f9": "terrorist groups",
+    "57302faa04bcaa1900d77311": "900,000",
+    "57302faa04bcaa1900d77312": "Islamist",
+    "57302faa04bcaa1900d77313": "2007",
+    "57302faa04bcaa1900d77314": "Londonistan",
+    "57302faa04bcaa1900d77315": "incitement to terrorism",
+    "57303048947a6a140053d254": "since 2001",
+    "57303048947a6a140053d255": "State Department",
+    "57303048947a6a140053d256": "Christian Whiton",
+    "57303048947a6a140053d257": "U.S. Defense Secretary",
+    "57303048947a6a140053d258": "undermining the communist ideology",
+    "573060b48ab72b1400f9c4c6": "Latin",
+    "573060b48ab72b1400f9c4c7": "colonization, use of military force",
+    "573060b48ab72b1400f9c4c9": "Japanese",
+    "573060b48ab72b1400f9c4ca": "technologies and ideas",
+    "573062662461fd1900a9cdf7": "influence",
+    "573062662461fd1900a9cdf8": "Formal",
+    "573062662461fd1900a9cdfa": "diplomacy or military force",
+    "573062662461fd1900a9cdfb": "direct",
+    "57306797396df919000960ee": "uneven trade agreements forcefully",
+    "57306797396df919000960ef": "full-fledged",
+    "57306797396df919000960f0": "aggressiveness",
+    "57306797396df919000960f2": "technological superiority, enforcing land officials into large debts that cannot be repaid, ownership of private industries",
+    "57306797396df919000960f1": "Informal",
+    "573081c2069b531400832133": "distinction of an empire is through the amount of land that a nation has conquered and expanded",
+    "573081c2069b531400832134": "world systems theory",
+    "573081c2069b531400832135": "Lenin",
+    "573081c2069b531400832136": "empires",
+    "573081c2069b531400832137": "sea and trade routes",
+    "573083dc2461fd1900a9ce6d": "colonialism",
+    "573083dc2461fd1900a9ce6e": "a political focus",
+    "573083dc2461fd1900a9ce6f": "ideological",
+    "573083dc2461fd1900a9ce70": "Ottoman",
+    "573083dc2461fd1900a9ce71": "a person or group of people",
+    "573085ea8ab72b1400f9c54c": "Imperialism and colonialism",
+    "573085ea8ab72b1400f9c54d": "taking physical control of another",
+    "573085ea8ab72b1400f9c54e": "by conquering the other state's lands and therefore increasing its own dominance",
+    "573085ea8ab72b1400f9c550": "the exploitation of the valuable assets and supplies of the nation that was conquered and the conquering nation then gaining the benefits from the spoils of the war",
+    "573085ea8ab72b1400f9c54f": "characteristics",
+    "5730876a396df9190009617a": "defense and justification of empire-building",
+    "5730876a396df9190009617c": "imperialism",
+    "5730876a396df9190009617b": "races of highest 'social efficiency'",
+    "5730876a396df9190009617d": "Social Darwinism",
+    "5730876a396df9190009617e": "whiteness",
+    "573088da069b53140083216b": "Germany",
+    "573088da069b53140083216c": "Britain",
+    "573088da069b53140083216d": "Political",
+    "573088da069b53140083216e": "expansion was necessary for a state\u2019s survival",
+    "573088da069b53140083216f": "a space for travellers to share these stories",
+    "57308cf88ab72b1400f9c576": "environmental determinism",
+    "57308cf88ab72b1400f9c577": "tropical",
+    "57308cf88ab72b1400f9c578": "Orientalism",
+    "57308cf88ab72b1400f9c579": "uncivilized people",
+    "57308cf88ab72b1400f9c57a": "the superior and the norm",
+    "57308ddc396df919000961a5": "Roman law",
+    "57308ddc396df919000961a7": "eighteenth century",
+    "57308ddc396df919000961a4": "British Empire",
+    "57308ddc396df919000961a6": "Aboriginal",
+    "57308ddc396df919000961a8": "empty land",
+    "57308f6b8ab72b1400f9c580": "geography",
+    "57308f6b8ab72b1400f9c581": "irrational and backward",
+    "57308f6b8ab72b1400f9c582": "irrational and backward",
+    "57308f6b8ab72b1400f9c583": "Orientalism",
+    "57308f6b8ab72b1400f9c584": "inferior",
+    "5730909d8ab72b1400f9c58a": "analysis of the role of nineteenth-century maps",
+    "5730909d8ab72b1400f9c58b": "blank space",
+    "5730909d8ab72b1400f9c58c": "unknown or unexplored territory",
+    "5730909d8ab72b1400f9c58d": "the role of nineteenth-century maps",
+    "5730909d8ab72b1400f9c58e": "French",
+    "573092088ab72b1400f9c595": "pre-Columbian era",
+    "573092088ab72b1400f9c594": "Genghis Khan",
+    "573092088ab72b1400f9c597": "dozens",
+    "573092088ab72b1400f9c598": "Oyo Empire",
+    "573092088ab72b1400f9c596": "Sub-Saharan Africa",
+    "573093598ab72b1400f9c5ae": "Cultural",
+    "573093598ab72b1400f9c5af": "when a country's influence is felt in social and cultural circles",
+    "573093598ab72b1400f9c5b0": "Dallas",
+    "573093598ab72b1400f9c5b1": "Roman",
+    "573093598ab72b1400f9c5b2": "bans on foreign popular culture, control of the internet and unauthorised satellite dishes",
+    "57309446396df919000961b8": "around 1700",
+    "57309446396df919000961b9": "colonizing, influencing, and annexing other parts of the world in order to gain political power",
+    "57309446396df919000961ba": "thousands",
+    "57309446396df919000961bb": "middle of the 20th century",
+    "57309446396df919000961bc": "Open Door Policy",
+    "57309564069b5314008321a5": "1919",
+    "57309564069b5314008321a6": "1999",
+    "57309564069b5314008321a7": "historians",
+    "57309564069b5314008321a8": "economy",
+    "57309564069b5314008321a9": "imperial powers",
+    "573098f38ab72b1400f9c5d2": "economic growth",
+    "573098f38ab72b1400f9c5d4": "mid-18th century",
+    "573098f38ab72b1400f9c5d3": "economic growth by collecting resources from colonies",
+    "573098f38ab72b1400f9c5d5": "the political weakness of the Mughal state",
+    "573099ee8ab72b1400f9c5dc": "communication",
+    "573099ee8ab72b1400f9c5dd": "deadly explosives",
+    "573099ee8ab72b1400f9c5de": "machine gun",
+    "573099ee8ab72b1400f9c5df": "arrows, swords, and leather shields",
+    "573099ee8ab72b1400f9c5e0": "Europe",
+    "57309bfb8ab72b1400f9c5e6": "British",
+    "57309bfb8ab72b1400f9c5e7": "late 1870s",
+    "57309bfb8ab72b1400f9c5e8": "philanthropy",
+    "57309bfb8ab72b1400f9c5e9": "need for capitalist economies to constantly expand investment, material resources and manpower",
+    "57309ef18ab72b1400f9c600": "aristocracy",
+    "57309ef18ab72b1400f9c602": "1950s",
+    "57309ef18ab72b1400f9c601": "1872\u20131967",
+    "57309ef18ab72b1400f9c603": "disease",
+    "57309ef18ab72b1400f9c604": "domestic social reforms could cure the international disease of imperialism by removing its economic foundation",
+    "5730a0778ab72b1400f9c60a": "environmental determinism",
+    "5730a0778ab72b1400f9c60b": "the environment in which they lived",
+    "5730a0778ab72b1400f9c60c": "less civilized",
+    "5730a0778ab72b1400f9c60d": "Africa",
+    "5730a0778ab72b1400f9c60e": "orientalism and tropicality",
+    "5730a314069b5314008321eb": "geographic scholars under colonizing empires",
+    "5730a314069b5314008321ec": "Northern Europe and the Mid-Atlantic",
+    "5730a314069b5314008321ed": "guidance and intervention",
+    "5730a314069b5314008321ee": "orientalism",
+    "5730a314069b5314008321ef": "colonizing empires",
+    "5730a40f396df91900096234": "sixteenth century",
+    "5730a40f396df91900096235": "1599",
+    "5730a40f396df91900096236": "Queen Elizabeth",
+    "5730a40f396df91900096237": "plundering of the local economy, almost bringing the company into bankruptcy",
+    "5730a40f396df91900096238": "Portuguese",
+    "5730a4d02461fd1900a9cf29": "1830",
+    "5730a4d02461fd1900a9cf2a": "after 1850",
+    "5730a4d02461fd1900a9cf2d": "Catholicism",
+    "5730a4d02461fd1900a9cf2b": "North and West Africa",
+    "5730a4d02461fd1900a9cf2c": "when Germany started to build her own colonial empire",
+    "5730a951069b531400832213": "civilize the inferior",
+    "5730a951069b531400832214": "Full citizenship rights",
+    "5730a951069b531400832215": "France sent small numbers of settlers to its colonies",
+    "5730a951069b531400832216": "Christianity and French culture",
+    "5730a951069b531400832217": "Algeria",
+    "5730aa52069b53140083221d": "overseas colonies",
+    "5730aa52069b53140083221e": "anti-colonial movements",
+    "5730aa52069b53140083221f": "Vietnam",
+    "5730aa52069b531400832220": "Vietnam",
+    "5730aa52069b531400832221": "1960",
+    "5730ab63396df91900096260": "Scandinavia and northern Europe",
+    "5730ab63396df91900096263": "Italy",
+    "5730ab63396df91900096261": "middle period of classical antiquity",
+    "5730ab63396df91900096262": "late antiquity",
+    "5730ab63396df91900096264": "central Europe",
+    "5730ac782461fd1900a9cf73": "late 19th century",
+    "5730ac782461fd1900a9cf74": "1862",
+    "5730ac782461fd1900a9cf75": "after the Franco-German War",
+    "5730ac782461fd1900a9cf77": "Napoleon",
+    "5730ac782461fd1900a9cf76": "Europe",
+    "5730aef38ab72b1400f9c682": "South Pacific",
+    "5730aef38ab72b1400f9c683": "German prestige",
+    "5730aef38ab72b1400f9c685": "1884",
+    "5730aef38ab72b1400f9c684": "German New Guinea",
+    "5730aef38ab72b1400f9c686": "Hamburg merchants and traders",
+    "5730b1022461fd1900a9cfa4": "Japan took part of Sakhalin Island from Russia",
+    "5730b1022461fd1900a9cfa3": "1894",
+    "5730b1022461fd1900a9cfa6": "Thailand",
+    "5730b1022461fd1900a9cfa5": "Manchuria",
+    "5730b255396df919000962b0": "The Soviet Union and the People\u2019s Republic of China",
+    "5730b255396df919000962b1": "1932",
+    "5730b255396df919000962b2": "Lenin",
+    "5730b255396df919000962b3": "Eastern Europe",
+    "5730b255396df919000962b4": "Bolshevik leaders",
+    "5730b4282461fd1900a9cfc5": "the revolution could only succeed in Russia as part of a world revolution",
+    "5730b4282461fd1900a9cfc6": "Lenin",
+    "5730b4282461fd1900a9cfc9": "Mao Zedong",
+    "5730b4282461fd1900a9cfc8": "Nikita Khrushchev",
+    "5730b4282461fd1900a9cfc7": "socialism in one country",
+    "5730b541396df919000962c2": "mercantilism",
+    "5730b541396df919000962c3": "1776",
+    "5730b541396df919000962c5": "free trade",
+    "5730b541396df919000962c4": "1820",
+    "5730b541396df919000962c6": "1815",
+    "5730b6592461fd1900a9cfcf": "British",
+    "5730b6592461fd1900a9cfd0": "pseudo-sciences",
+    "5730b6592461fd1900a9cfd2": "British Empire",
+    "5730b6592461fd1900a9cfd1": "Middle East",
+    "5730b7ce069b5314008322c3": "US annexation of the Philippines and Cuba",
+    "5730b7ce069b5314008322c4": "interventionism in Central America",
+    "5730b7ce069b5314008322c6": "a war",
+    "5730b7ce069b5314008322c5": "Philippines",
+    "5730b7ce069b5314008322c7": "racket",
+    "5730b8ca8ab72b1400f9c704": "Isiah Bowman",
+    "5730b8ca8ab72b1400f9c705": "1917",
+    "5730b8ca8ab72b1400f9c706": "President Wilson and the American delegation from the Paris Peace Conference",
+    "5730b8ca8ab72b1400f9c707": "to build a premise that would allow for U.S authorship of a 'new world'",
+    "5730b8ca8ab72b1400f9c708": "Wilson's geographer",
+    "5730bb522461fd1900a9d011": "internal strife",
+    "5730bb522461fd1900a9d012": "internal colonialism",
+    "5730bb522461fd1900a9d013": "12 to 15 million",
+    "5730bb522461fd1900a9d015": "indigenous peoples and African-Americans",
+    "5730bc308ab72b1400f9c73c": "1299 to 1923",
+    "5730bc308ab72b1400f9c73d": "Suleiman the Magnificent",
+    "5730bc308ab72b1400f9c73e": "32",
+    "5730bc308ab72b1400f9c73f": "Horn of Africa",
+    "5730bc308ab72b1400f9c740": "16th and 17th centuries",
+    "5730bd00069b5314008322d7": "Istanbul",
+    "5730bd00069b5314008322d9": "Germany",
+    "5730bd00069b5314008322db": "creation of modern Balkan and Middle Eastern states",
+    "5730bd00069b5314008322da": "Turkey",
+    "5730982f396df919000961e2": "The United Methodist Church",
+    "5730982f396df919000961e3": "a mainline Protestant Methodist denomination",
+    "5730982f396df919000961e4": "1968",
+    "5730982f396df919000961e5": "the union of the Methodist Church (USA) and the Evangelical United Brethren Church",
+    "5730982f396df919000961e6": "Wesleyan",
+    "573098c12461fd1900a9cee5": "The United Methodist Church",
+    "573098c12461fd1900a9cee6": "80 million",
+    "573098c12461fd1900a9cee7": "mainline Protestant denomination",
+    "573098c12461fd1900a9cee8": "3.6%",
+    "57309921396df919000961f6": "mid-18th century",
+    "57309921396df919000961f7": "Church of England",
+    "57309921396df919000961f8": "being methodical and exceptionally detailed in their Bible study, opinions and disciplined lifestyle",
+    "573099f42461fd1900a9cef7": "1735",
+    "573099f42461fd1900a9cef8": "the colony of Georgia",
+    "573099f42461fd1900a9cef9": "the American Indians",
+    "573099f42461fd1900a9cefa": "salvation by God's grace",
+    "57309a6c2461fd1900a9ceff": "American Revolution",
+    "57309a6c2461fd1900a9cf00": "1784",
+    "57309a6c2461fd1900a9cf01": "Thomas Coke",
+    "57309a6c2461fd1900a9cf02": "Lovely Lane Methodist Church",
+    "57309a6c2461fd1900a9cf03": "Lovely Lane Methodist Church",
+    "57309adb396df919000961fc": "St. George's United Methodist Church",
+    "57309adb396df919000961fd": "St. George's United Methodist Church",
+    "57309adb396df919000961fe": "1767",
+    "57309adb396df919000961ff": "a sail loft on Dock Street",
+    "57309adb396df91900096200": "1784",
+    "57309cd6069b5314008321c3": "Richard Allen and Absalom Jones",
+    "57309cd6069b5314008321c4": "St. George's Church",
+    "57309cd6069b5314008321c5": "1784",
+    "57309d31396df91900096210": "1830",
+    "57309d31396df91900096211": "over the issue of laity having a voice and vote in the administration of the church",
+    "57309d31396df91900096212": "1844",
+    "57309d31396df91900096213": "tensions over slavery and the power of bishops in the denomination",
+    "5730a97a396df9190009625a": "April 23, 1968",
+    "5730a97a396df9190009625b": "Dallas, Texas",
+    "5730a97a396df9190009625c": "Bishop Lloyd Christ Wicke",
+    "5730a9fa2461fd1900a9cf6d": "holy catholic (or universal) church",
+    "5730a9fa2461fd1900a9cf6e": "The Book of Discipline",
+    "5730a9fa2461fd1900a9cf6f": "all who are truly believers in every age belong to the holy Church invisible",
+    "5730aaa88ab72b1400f9c64e": "As a result of the American Revolution",
+    "5730aaa88ab72b1400f9c650": "Dr. Thomas Coke",
+    "5730aaa88ab72b1400f9c64f": "Thomas Vasey and Richard Whatcoat",
+    "5730ab288ab72b1400f9c655": "1968",
+    "5730ab288ab72b1400f9c654": "John Wesley and Charles Wesley",
+    "5730ab288ab72b1400f9c656": "Albert C. Outler",
+    "5730ab288ab72b1400f9c657": "Albert C. Outler",
+    "5730ac6b8ab72b1400f9c670": "Prevenient grace",
+    "5730ac6b8ab72b1400f9c672": "Prevenient grace",
+    "5730ac6b8ab72b1400f9c671": "the grace that \"goes before\" us",
+    "5730ac6b8ab72b1400f9c673": "sin",
+    "5730aeba069b531400832241": "Justifying Grace or Accepting Grace",
+    "5730aeba069b531400832242": "The justifying grace",
+    "5730aeba069b531400832243": "conversion",
+    "5730aeba069b531400832244": "conversion",
+    "5730aeba069b531400832245": "the New Birth",
+    "5730afed069b53140083225f": "that grace of God",
+    "5730afed069b531400832260": "Sanctifying Grace",
+    "5730afed069b531400832261": "a genuine love of God with heart, soul, mind, and strength, and a genuine love of our neighbors as ourselves",
+    "5730afed069b531400832262": "Sanctifying Grace",
+    "5730b07c8ab72b1400f9c696": "Wesleyan theology",
+    "5730b07c8ab72b1400f9c697": "prima scriptura",
+    "5730b07c8ab72b1400f9c698": "UMC",
+    "5730b07c8ab72b1400f9c699": "The Book of Discipline",
+    "5730b10b8ab72b1400f9c69f": "2008",
+    "5730b10b8ab72b1400f9c69e": "pro-choice",
+    "5730b10b8ab72b1400f9c6a0": "Religious Coalition for Reproductive Choice",
+    "5730b1e28ab72b1400f9c6ae": "The General Board of Church and Society, and the United Methodist Women",
+    "5730b1e28ab72b1400f9c6af": "all women",
+    "5730b1e28ab72b1400f9c6b0": "the mother",
+    "5730b2312461fd1900a9cfad": "Taskforce of United Methodists on Abortion and Sexuality",
+    "5730b2312461fd1900a9cfae": "2012",
+    "5730b2312461fd1900a9cfaf": "Rev. Paul T. Stallsworth",
+    "5730b2ac2461fd1900a9cfb3": "temperance movement",
+    "5730b2ac2461fd1900a9cfb6": "2011 and 2012",
+    "5730b2ac2461fd1900a9cfb4": "The Use of Money",
+    "5730b2ac2461fd1900a9cfb5": "unfermented grape juice",
+    "5730b54c069b53140083228d": "capital punishment",
+    "5730b54c069b53140083228e": "John 8:7",
+    "5730b54c069b53140083228f": "Matthew 5:38-39",
+    "5730b54c069b531400832290": "The General Conference of the United Methodist Church",
+    "5730b5cc396df919000962d2": "same-sex unions",
+    "5730b5cc396df919000962d3": "1999",
+    "5730b5cc396df919000962d4": "2016",
+    "5730b6bd2461fd1900a9cfd7": "the Connectional Table",
+    "5730b6bd2461fd1900a9cfd8": "LGBT",
+    "5730b6bd2461fd1900a9cfd9": "same-gender marriages with resolutions",
+    "5730b776069b5314008322bd": "1987",
+    "5730b776069b5314008322be": "2005",
+    "5730b776069b5314008322bf": "The Baltimore-Washington Conference",
+    "5730b9852461fd1900a9cff9": "conscription",
+    "5730b9852461fd1900a9cffa": "military action, nor the way of inaction",
+    "5730b9852461fd1900a9cffb": "all war",
+    "5730b9dc8ab72b1400f9c70e": "Christ's message and teachings",
+    "5730b9dc8ab72b1400f9c70f": "national foreign policy",
+    "5730b9dc8ab72b1400f9c710": "general and complete disarmament",
+    "5730bdfe396df9190009630e": "Sexual Ethics Task Force",
+    "5730bdfe396df9190009630f": "violence, degradation, exploitation, and coercion",
+    "5730bdfe396df91900096310": "girls and women",
+    "5730bf03069b5314008322eb": "IVF",
+    "5730bf03069b5314008322ed": "stem cells",
+    "5730bf03069b5314008322ec": "research",
+    "5730c059069b531400832305": "the Sunday Service of the Methodists in North America",
+    "5730c059069b531400832306": "When the Methodists in America were separated from the Church of England",
+    "5730c059069b531400832307": "Book of Common Prayer",
+    "5730c52fb54a4f140068cc46": "Africa",
+    "5730c52fb54a4f140068cc47": "Book of Common Prayer",
+    "5730c52fb54a4f140068cc48": "anointing with oil",
+    "5730c6d3b54a4f140068cc4c": "Methodist institutions",
+    "5730c6d3b54a4f140068cc4d": "William Booth",
+    "5730c6d3b54a4f140068cc4e": "John Wesley",
+    "5730c737b54a4f140068cc52": "United Methodist Church",
+    "5730c737b54a4f140068cc54": "General Conference",
+    "5730c737b54a4f140068cc55": "Book of Discipline",
+    "5730c737b54a4f140068cc53": "General Conference",
+    "5730c737b54a4f140068cc56": "every four years",
+    "5730c810aca1c71400fe5aa7": "five",
+    "5730c810aca1c71400fe5aa8": "seven",
+    "5730c810aca1c71400fe5aa9": "to elect and appoint bishops",
+    "5730c810aca1c71400fe5aaa": "bishops",
+    "5730c810aca1c71400fe5aab": "Bishops thus elected serve Episcopal Areas",
+    "5730c8a1f6cb411900e2449c": "Mission Council",
+    "5730c8a1f6cb411900e2449d": "church bishops",
+    "5730c8a1f6cb411900e2449e": "36",
+    "5730c8a1f6cb411900e2449f": "George W. Bush Presidential Library",
+    "5730c8a1f6cb411900e244a0": "Southern Methodist University",
+    "5730ca6eaca1c71400fe5ac2": "nine",
+    "5730ca6eaca1c71400fe5ac1": "The Judicial Council",
+    "5730ca6eaca1c71400fe5ac3": "eight-year term",
+    "5730ca6eaca1c71400fe5ac4": "twice",
+    "5730ca6eaca1c71400fe5ac5": "various locations throughout the world",
+    "5730cb0fb7151e1900c0154a": "The Annual Conference",
+    "5730cb0fb7151e1900c0154b": "the geographical area it covers as well as the frequency of meeting",
+    "5730cb0fb7151e1900c0154c": "Annual Conference",
+    "5730cb8df6cb411900e244c2": "The Book of Discipline",
+    "5730cb8df6cb411900e244c3": "three",
+    "5730cb8df6cb411900e244c4": "three",
+    "5730cb8df6cb411900e244c5": "The church conference",
+    "5730cb8df6cb411900e244c6": "The church conference",
+    "5730cc2cb54a4f140068cc66": "one hundred",
+    "5730cc2cb54a4f140068cc67": "three hundred sixty",
+    "5730cc2cb54a4f140068cc68": "International Association of Methodist-related Schools, Colleges, and Universities",
+    "5730d26fb54a4f140068cc94": "John Wesley",
+    "5730d26fb54a4f140068cc95": "pastors",
+    "5730d26fb54a4f140068cc96": "Annual Conference Order of Elders",
+    "5730d26fb54a4f140068cc97": "their Annual Conference Order of Deacons",
+    "5730d3adaca1c71400fe5af5": "Annual Conference Cabinet",
+    "5730d3adaca1c71400fe5af6": "annually",
+    "5730d3adaca1c71400fe5af7": "the bishop has read the appointments at the session of the Annual Conference",
+    "5730d473b7151e1900c0155a": "Elders",
+    "5730d473b7151e1900c0155b": "a bishop",
+    "5730d473b7151e1900c0155c": "2\u20133",
+    "5730d473b7151e1900c0155d": "District Superintendents",
+    "5730d523aca1c71400fe5b03": "2\u20133",
+    "5730d523aca1c71400fe5b04": "Deacons",
+    "5730d523aca1c71400fe5b05": "Deacons",
+    "5730d523aca1c71400fe5b06": "sacramental authority",
+    "5730d597f6cb411900e244d6": "1996",
+    "5730d597f6cb411900e244d7": "provisional elder/deacon",
+    "5730d597f6cb411900e244d8": "the ordination order of transitional deacon",
+    "5730d6fcb7151e1900c01562": "Licensed Local Pastor",
+    "5730d6fcb7151e1900c01563": "The licensed local pastor",
+    "5730d6fcb7151e1900c01564": "five",
+    "5730d6fcb7151e1900c01565": "Associate",
+    "5730d77ef6cb411900e244dc": "Baptized Members",
+    "5730d77ef6cb411900e244dd": "through confirmation and sometimes the profession of faith",
+    "5730d77ef6cb411900e244de": "transfer from another Christian denomination",
+    "5730de74f6cb411900e244fc": "Baptism",
+    "5730de74f6cb411900e244fd": "confirmation and membership preparation classes",
+    "5730de74f6cb411900e244fe": "The Book of Discipline of the United Methodist Church",
+    "5730de74f6cb411900e244ff": "Church and the Methodist-Christian theological tradition",
+    "5730e936aca1c71400fe5b61": "lay servants",
+    "5730e936aca1c71400fe5b62": "they must be recommended by their pastor and Church Council or Charge Conference, and complete the basic course for lay servant",
+    "5730e936aca1c71400fe5b63": "annually",
+    "5730e936aca1c71400fe5b64": "two",
+    "5730e9f4aca1c71400fe5b73": "The United Methodist Church",
+    "5730e9f4aca1c71400fe5b74": "observer",
+    "5730e9f4aca1c71400fe5b75": "blurring of theological and confessional differences in the interests of unity",
+    "5730ea71b54a4f140068cce4": "2000",
+    "5730ea71b54a4f140068cce5": "May 2012",
+    "5730ea71b54a4f140068cce6": "1985",
+    "5730eb5b497a881900248a3f": "11 million",
+    "5730eb5b497a881900248a40": "42,000",
+    "5730eb5b497a881900248a41": "8 million",
+    "5730eb5b497a881900248a42": "34,000",
+    "5730eb5b497a881900248a43": "Texas",
+    "5730ebe0a5e9cc1400cdbae5": "11.4 million",
+    "5730ebe0a5e9cc1400cdbae6": "11.4 million, with about 7.9 million",
+    "5730ebe0a5e9cc1400cdbae7": "11.4 million",
+    "5730ec85e6313a140071caba": "Wesleyan Holiness Consortium",
+    "5730ec85e6313a140071cabb": "World Methodist Council",
+    "5730ec85e6313a140071cabc": "July 18, 2006",
+    "5733cf61d058e614000b62e9": "1754\u20131763",
+    "5733cf61d058e614000b62ea": "British America and New France",
+    "5733cf61d058e614000b62eb": "60,000",
+    "5733cf61d058e614000b62ec": "2 million",
+    "5733cff84776f419006612b4": "along the frontiers between New France and the British colonies",
+    "5733cff84776f419006612b5": "a dispute over control of the confluence of the Allegheny and Monongahela rivers",
+    "5733cff84776f419006612b6": "May 1754",
+    "5733d13e4776f419006612c4": "1755",
+    "5733d13e4776f419006612c5": "a disaster",
+    "5733d13e4776f419006612c6": "poor management, internal divisions, and effective Canadian scouts, French regular forces, and Indian warrior allies",
+    "5733d13e4776f419006612c7": "Fort Beaus\u00e9jour",
+    "5733d13e4776f419006612c8": "expulsion of the Acadians",
+    "5733d249d058e614000b6331": "William Pitt",
+    "5733d249d058e614000b6332": "France was unwilling to risk large convoys",
+    "5733d249d058e614000b6333": "European theatre of the war",
+    "5733d249d058e614000b6334": "Sainte Foy in Quebec",
+    "5733d2dbd058e614000b6339": "French Louisiana",
+    "5733d2dbd058e614000b633a": "French Louisiana",
+    "5733d2dbd058e614000b633b": "one of the most significant developments in a century of Anglo-French conflict",
+    "5733d3cb4776f419006612ea": "1740s",
+    "5733d3cb4776f419006612eb": "Indians fought on both sides of the conflict",
+    "5733d3cb4776f419006612ec": "a much larger conflict between France and Great Britain",
+    "5733d3cb4776f419006612ed": "the Fourth Intercolonial War and the Great War for the Empire",
+    "5733d4c8d058e614000b6353": "from the official declaration of war in 1756 to the signing of the peace treaty in 1763",
+    "5733d4c8d058e614000b6354": "six years",
+    "5733d4c8d058e614000b6355": "1760",
+    "5733d4c8d058e614000b6356": "Battle of Jumonville Glen",
+    "5733d5704776f4190066130e": "75,000",
+    "5733d5704776f4190066130f": "St. Lawrence River valley",
+    "5733d5704776f41900661310": "St. Lawrence and Mississippi watersheds",
+    "5733d68ed058e614000b637f": "20 to 1",
+    "5733d68ed058e614000b6380": "Nova Scotia and Newfoundland",
+    "5733d68ed058e614000b6381": "along the coast",
+    "5733d7cbd058e614000b63ab": "native tribes",
+    "5733d7cbd058e614000b63ac": "Mi'kmaq and the Abenaki",
+    "5733d7cbd058e614000b63ad": "Upstate New York and the Ohio Country",
+    "5733d7cbd058e614000b63ae": "Iroquois rule",
+    "5733da01d058e614000b63f9": "Catawba",
+    "5733da01d058e614000b63fa": "western portions of the Great Lakes region",
+    "5733da01d058e614000b63fb": "Iroquois Six Nations",
+    "5733dab4d058e614000b6409": "no French regular army troops were stationed in North America, and few British troops",
+    "5733dab4d058e614000b640a": "few British troops",
+    "5733dab4d058e614000b640b": "Most British colonies mustered local militia companies, generally ill trained and available only for short periods, to deal with native threats",
+    "5733db8dd058e614000b6428": "The expedition covered about 3,000 miles",
+    "5733db8dd058e614000b6429": "200 Troupes de la marine and 30 Indians",
+    "5733db8dd058e614000b642a": "Whenever he encountered British merchants or fur-traders",
+    "5733dc95d058e614000b644a": "informed C\u00e9loron that they owned the Ohio Country and that they would trade with the British",
+    "5733dc95d058e614000b644b": "Pickawillany",
+    "5733dc95d058e614000b644c": "threatened \"Old Briton\" with severe consequences",
+    "5733dc95d058e614000b644d": "ignored the warning",
+    "5733dd4f4776f419006613aa": "very badly disposed towards the French, and are entirely devoted to the English",
+    "5733dd4f4776f419006613ab": "brisk trade in European colonial captives",
+    "5733dd4f4776f419006613ac": "forceful",
+    "5733e5a14776f4190066145b": "1749",
+    "5733e5a14776f4190066145c": "Ohio Company of Virginia",
+    "5733e5a14776f4190066145d": "Christopher Gist",
+    "5733e5a14776f4190066145e": "Treaty of Logstown",
+    "5733e5a14776f4190066145f": "the mouth of the Monongahela River",
+    "5733e771d058e614000b6545": "King George's War",
+    "5733e771d058e614000b6546": "1748",
+    "5733e771d058e614000b6547": "conflicting territorial claims between British and French colonies in North America",
+    "5733e771d058e614000b6548": "The issues of conflicting territorial claims between British and French colonies in North America were turned over to a commission to resolve",
+    "5733e8ae4776f419006614a6": "Marquis de la Jonqui\u00e8re",
+    "5733e8ae4776f419006614a7": "300",
+    "5733e8ae4776f419006614a8": "His objective was to punish the Miami people of Pickawillany for not following C\u00e9loron's orders to cease trading with the British",
+    "5733e8ae4776f419006614a9": "capturing three traders",
+    "5733ea04d058e614000b6594": "Paul Marin de la Malgue",
+    "5733ea04d058e614000b6595": "Fort Presque Isle",
+    "5733ea04d058e614000b6596": "Fort Le Boeuf",
+    "5733ea04d058e614000b6597": "protect the King's land in the Ohio Valley from the British",
+    "5733ea04d058e614000b6598": "Tanaghrisson",
+    "5733eb34d058e614000b65cc": "Superintendent for Indian Affairs",
+    "5733eb34d058e614000b65cd": "Warraghiggey",
+    "5733eb34d058e614000b65ce": "colonel",
+    "5733eb34d058e614000b65cf": "Mohawk Chief Hendrick",
+    "5733ef47d058e614000b662b": "Ohio Company",
+    "5733ef47d058e614000b662c": "Major George Washington",
+    "5733ef47d058e614000b662d": "Jacob Van Braam",
+    "5733ef47d058e614000b662e": "December 12",
+    "5733f062d058e614000b6633": "Jacques Legardeur de Saint-Pierre",
+    "5733f062d058e614000b6634": "the letter from Dinwiddie",
+    "5733f062d058e614000b6635": "\"As to the Summons you send me to retire, I do not think myself obliged to obey it.\"",
+    "5733f062d058e614000b6636": "superior to that of the British",
+    "5733f1784776f41900661575": "500",
+    "5733f1784776f41900661576": "1754",
+    "5733f1784776f41900661577": "Fort Duquesne",
+    "5733f309d058e614000b6648": "surprised the Canadians",
+    "5733f309d058e614000b6649": "Canadians",
+    "5733f309d058e614000b664a": "to gain the support of the British and regain authority over his own people",
+    "5733f410d058e614000b6663": "to dislodge the French",
+    "5733f410d058e614000b6664": "well before Braddock's departure for North America.",
+    "5733f410d058e614000b6665": "dispatched six regiments to New France under the command of Baron Dieskau in 1755",
+    "5733f410d058e614000b6666": "blockade French ports",
+    "5733f5264776f419006615a3": "Albany Congress",
+    "5733f5264776f419006615a4": "to formalize a unified front in trade and negotiations with various Indians",
+    "5733f5264776f419006615a5": "The plan that the delegates agreed to was never ratified by the colonial legislatures nor approved of by the crown",
+    "5733f5264776f419006615a6": "to formalize a unified front in trade and negotiations with various Indians",
+    "5733f5f24776f419006615c1": "Braddock",
+    "5733f5f24776f419006615c2": "The expedition was a disaster",
+    "5733f5f24776f419006615c3": "1,000",
+    "5733f5f24776f419006615c4": "Washington and Thomas Gage",
+    "5733f7b9d058e614000b66a9": "Shirley and Johnson",
+    "5733f7b9d058e614000b66aa": "logistical difficulties",
+    "5733f7b9d058e614000b66ab": "Fort Niagara",
+    "5733f7b9d058e614000b66ac": "garrisons",
+    "5733f8dc4776f419006615f7": "Marquis de Vaudreuil",
+    "5733f8dc4776f419006615f8": "sent Dieskau to Fort St. Fr\u00e9d\u00e9ric to meet that threat",
+    "5733f8dc4776f419006615f9": "Fort Edward and Fort William Henry",
+    "5733f8dc4776f419006615fa": "Fort William Henry",
+    "5733f8dc4776f419006615fb": "Ticonderoga Point",
+    "5733f9fa4776f4190066161f": "Colonel Monckton",
+    "5733f9fa4776f41900661620": "ordered the deportation of the French-speaking Acadian population from the area.",
+    "5733f9fa4776f41900661621": "only clashes of any size were at Petitcodiac in 1755 and at Bloody Creek near Annapolis Royal in 1757",
+    "5733faaf4776f4190066162f": "William Shirley",
+    "5733faaf4776f41900661630": "Albany",
+    "5733faaf4776f41900661631": "Fort Frontenac",
+    "5733faaf4776f41900661632": "through the wilderness of the Maine district",
+    "5733fb7bd058e614000b66ff": "Major General James Abercrombie",
+    "5733fb7bd058e614000b6700": "Major General Louis-Joseph de Montcalm",
+    "5733fb7bd058e614000b6701": "May 18, 1756",
+    "5733fc6ed058e614000b670f": "Oneida Carry",
+    "5733fc6ed058e614000b6710": "supply chain",
+    "5733fc6ed058e614000b6711": "45,000 pounds",
+    "5733fc6ed058e614000b6712": "campaigns",
+    "5733fd66d058e614000b6735": "Abercrombie",
+    "5733fd66d058e614000b6736": "Ticonderoga",
+    "5733fd66d058e614000b6737": "Albany",
+    "5733fd66d058e614000b6738": "disposition of prisoners' personal effects",
+    "5733fe73d058e614000b673d": "an attack on New France's capital, Quebec",
+    "5733fe73d058e614000b673e": "to distract Montcalm",
+    "5733fe73d058e614000b673f": "William Pitt",
+    "5733fe73d058e614000b6740": "Loudoun returned to New York",
+    "5733ffa7d058e614000b674f": "French irregular forces",
+    "5733ffa7d058e614000b6750": "Lake George",
+    "5733ffa7d058e614000b6751": "attacked the British column",
+    "57340111d058e614000b677d": "a poor harvest",
+    "57340111d058e614000b677e": "poor harvest",
+    "57340111d058e614000b677f": "defense of the St. Lawrence",
+    "5734025d4776f419006616c3": "The British failures in North America",
+    "5734025d4776f419006616c4": "Pitt",
+    "5734025d4776f419006616c5": "three major offensive actions",
+    "5734025d4776f419006616c6": "Two",
+    "573403394776f419006616dd": "3,600",
+    "573403394776f419006616de": "3,600",
+    "573403394776f419006616df": "saved something",
+    "573403394776f419006616e0": "saved something",
+    "57340549d058e614000b67dd": "an invasion of Britain",
+    "57340549d058e614000b67de": "poor",
+    "57340549d058e614000b67df": "Lagos and Quiberon Bay",
+    "573406d1d058e614000b6801": "James Wolfe",
+    "573406d1d058e614000b6802": "successfully cut off the French frontier forts further to the west and south",
+    "573406d1d058e614000b6803": "Battle of Sainte-Foy",
+    "573406d1d058e614000b6804": "the British were able to prevent the arrival of French relief ships in the naval Battle of the Restigouche",
+    "573407d7d058e614000b6813": "Governor Vaudreuil",
+    "573407d7d058e614000b6814": "freedom to continue worshiping in their Roman Catholic tradition, continued ownership of their property, and the right to remain undisturbed in their homes",
+    "573407d7d058e614000b6815": "General Amherst",
+    "573408ef4776f41900661757": "10 February 1763",
+    "573408ef4776f41900661758": "15 February 1763",
+    "573408ef4776f41900661759": "France chose to cede the former",
+    "573408ef4776f4190066175a": "France chose to cede the former",
+    "57340a094776f4190066177d": "80,000",
+    "57340a094776f4190066177e": "1755",
+    "57340a094776f4190066177f": "North American provinces",
+    "57340a094776f41900661780": "New Orleans",
+    "57340b1bd058e614000b6869": "King George III",
+    "57340b1bd058e614000b686a": "the division and administration of the newly conquered territory",
+    "57340b1bd058e614000b686b": "west of the Appalachian Mountains",
+    "57340d124776f419006617c3": "Cuba",
+    "57340d124776f419006617c0": "construction of military roads to the area by Braddock and Forbes",
+    "57340d124776f419006617c1": "1769",
+    "57340d124776f419006617c2": "Choctaw and the Creek",
+    "57340d124776f419006617bf": "disappearance of a strong ally and counterweight to British expansion",
+    "573735e8c3c5551400e51e71": "force",
+    "573735e8c3c5551400e51e72": "A fundamental error",
+    "573735e8c3c5551400e51e73": "Sir Isaac Newton",
+    "573735e8c3c5551400e51e74": "three hundred years",
+    "573735e8c3c5551400e51e75": "Sir Isaac Newton",
+    "57373a9fc3c5551400e51e7b": "a Standard Model",
+    "57373a9fc3c5551400e51e7c": "gauge bosons",
+    "57373a9fc3c5551400e51e7d": "electromagnetic, weak, and gravitational",
+    "57373a9fc3c5551400e51e7e": "electromagnetic, weak, and gravitational",
+    "57373a9fc3c5551400e51e7f": "electroweak interaction",
+    "57373d0cc3c5551400e51e85": "Aristotle",
+    "57373d0cc3c5551400e51e86": "Aristotelian cosmology",
+    "57373d0cc3c5551400e51e87": "four",
+    "57373d0cc3c5551400e51e88": "on the ground",
+    "57373d0cc3c5551400e51e89": "natural motion",
+    "57373f80c3c5551400e51e8f": "17th century",
+    "57373f80c3c5551400e51e90": "Galileo Galilei",
+    "57373f80c3c5551400e51e91": "an innate force of impetus",
+    "57373f80c3c5551400e51e92": "Galileo Galilei",
+    "57373f80c3c5551400e51e93": "friction",
+    "5737432bc3c5551400e51e99": "Newton",
+    "5737432bc3c5551400e51e9a": "a lack of net force",
+    "5737432bc3c5551400e51e9b": "Newton",
+    "5737432bc3c5551400e51e9c": "Newton's First Law",
+    "5737432bc3c5551400e51e9d": "the laws of physics are the same in every inertial frame of reference",
+    "5737477bc3c5551400e51ea3": "the laws of physics",
+    "5737477bc3c5551400e51ea4": "parabolic path",
+    "5737477bc3c5551400e51ea5": "at rest",
+    "5737477bc3c5551400e51ea6": "Inertia",
+    "573749741c4567190057445d": "inertia",
+    "573749741c4567190057445e": "rotational inertia",
+    "573749741c4567190057445f": "Albert Einstein",
+    "573749741c45671900574460": "weightlessness",
+    "573749741c45671900574461": "principle of equivalence",
+    "573750f51c45671900574467": "Newton's Second Law",
+    "573750f61c45671900574468": "kinematic",
+    "573750f61c45671900574469": "General relativity",
+    "573750f61c4567190057446a": "General relativity",
+    "573750f61c4567190057446b": "relative units of force and mass then are fixed",
+    "5737534ec3c5551400e51eab": "Newton's Third Law",
+    "5737534ec3c5551400e51eac": "Newton's Third Law",
+    "5737534ec3c5551400e51ead": "unidirectional force",
+    "5737534ec3c5551400e51eae": "The third law",
+    "573755afc3c5551400e51eb3": "the center of mass",
+    "573755afc3c5551400e51eb4": "closed",
+    "573755afc3c5551400e51eb5": "the mass of the system",
+    "573766251c45671900574471": "intuitive understanding",
+    "573766251c45671900574472": "precise operational definitions",
+    "573766251c45671900574473": "Newtonian mechanics",
+    "573766251c45671900574474": "Through experimentation",
+    "57376828c3c5551400e51eb9": "vector quantities",
+    "57376828c3c5551400e51eba": "scalar quantities",
+    "57376828c3c5551400e51ebb": "Associating forces with vectors",
+    "57376828c3c5551400e51ebc": "ambiguous",
+    "57376828c3c5551400e51ebd": "impossible",
+    "57376a1bc3c5551400e51ec3": "static equilibrium",
+    "57376a1bc3c5551400e51ec4": "magnitude and direction",
+    "57376a1bc3c5551400e51ec5": "net force",
+    "57376a1bc3c5551400e51ec6": "their respective lines of application",
+    "57376a1bc3c5551400e51ec7": "parallelogram",
+    "57376c50c3c5551400e51ecd": "independent components",
+    "57376c50c3c5551400e51ece": "two",
+    "57376c50c3c5551400e51ecf": "the original force",
+    "57376c50c3c5551400e51ed0": "orthogonal components",
+    "57376c50c3c5551400e51ed1": "independent components",
+    "57376df3c3c5551400e51ed7": "static friction",
+    "57376df3c3c5551400e51ed8": "static friction",
+    "57376df3c3c5551400e51ed9": "acceleration",
+    "57376df3c3c5551400e51eda": "increases or decreases",
+    "57377083c3c5551400e51edf": "force of gravity",
+    "57377083c3c5551400e51ee0": "spring reaction force",
+    "57377083c3c5551400e51ee1": "weight",
+    "57377083c3c5551400e51ee2": "force of gravity",
+    "57377083c3c5551400e51ee3": "Isaac Newton",
+    "573776eec3c5551400e51ee9": "Galileo",
+    "573776eec3c5551400e51eea": "rest",
+    "573776eec3c5551400e51eeb": "Aristotle",
+    "573776eec3c5551400e51eec": "behind the foot of the mast of a moving ship",
+    "573776eec3c5551400e51eed": "behind the foot of the mast of a moving ship",
+    "57377862c3c5551400e51ef3": "dynamic equilibrium",
+    "57377862c3c5551400e51ef4": "kinetic friction force",
+    "57377862c3c5551400e51ef5": "kinetic friction",
+    "57377862c3c5551400e51ef6": "Aristotle",
+    "57377aac1c45671900574479": "Schr\u00f6dinger equation",
+    "57377aac1c4567190057447a": "Newtonian",
+    "57377aac1c4567190057447b": "classical position variables",
+    "57377aac1c4567190057447c": "quantized",
+    "57377aac1c4567190057447d": "force",
+    "57377c98c3c5551400e51efb": "spin",
+    "57377c98c3c5551400e51efc": "Pauli principle",
+    "57377c98c3c5551400e51efd": "value of the spin",
+    "57377c98c3c5551400e51efe": "symmetric",
+    "57377c98c3c5551400e51eff": "symmetric",
+    "57377ec7c3c5551400e51f05": "a mathematical by-product of exchange of momentum-carrying",
+    "57377ec7c3c5551400e51f06": "force",
+    "57377ec7c3c5551400e51f07": "conservation of momentum",
+    "57377ec7c3c5551400e51f08": "Feynman diagrams",
+    "57377ec7c3c5551400e51f09": "straight",
+    "5737804dc3c5551400e51f0f": "four",
+    "5737804dc3c5551400e51f10": "strong and weak forces",
+    "5737804dc3c5551400e51f11": "electromagnetic force",
+    "5737804dc3c5551400e51f12": "masses",
+    "5737804dc3c5551400e51f13": "Pauli exclusion principle",
+    "5737821cc3c5551400e51f19": "Isaac Newton",
+    "5737821cc3c5551400e51f1a": "20th",
+    "5737821cc3c5551400e51f1b": "unification models",
+    "5737821cc3c5551400e51f1c": "self-consistent unification",
+    "573784fa1c45671900574483": "Isaac Newton",
+    "573784fa1c45671900574484": "Galileo",
+    "573784fa1c45671900574485": "9.81 meters per second",
+    "573784fa1c45671900574486": "sea level",
+    "573784fa1c45671900574487": "the force of gravity on an object at the Earth's surface is directly proportional to the object's mass",
+    "573786b51c4567190057448d": "larger distances",
+    "573786b51c4567190057448e": "the same force of gravity",
+    "573786b51c4567190057448f": "the mass of the attracting body",
+    "573786b51c45671900574490": "the radius",
+    "57378862c3c5551400e51f21": "a dimensional constant",
+    "57378862c3c5551400e51f22": "Henry Cavendish",
+    "57378862c3c5551400e51f23": "1798",
+    "57378862c3c5551400e51f24": "Newton",
+    "5737898f1c45671900574495": "Mercury",
+    "5737898f1c45671900574496": "Vulcan",
+    "5737898f1c45671900574497": "theory of general relativity",
+    "5737898f1c45671900574498": "Albert Einstein",
+    "5737898f1c45671900574499": "Albert Einstein",
+    "57378b141c4567190057449f": "general relativity",
+    "57378b141c456719005744a0": "straight line path",
+    "57378b141c456719005744a1": "gravitational force",
+    "57378b141c456719005744a2": "global",
+    "57378c9b1c456719005744a8": "electric current",
+    "57378c9b1c456719005744a9": "electricity and magnetism allows for the description of a unified electromagnetic force",
+    "57378c9b1c456719005744a7": "Lorentz's Law",
+    "57378c9b1c456719005744aa": "the magnetic force",
+    "57378e311c456719005744af": "James Clerk Maxwell",
+    "57378e311c456719005744b0": "1864",
+    "57378e311c456719005744b1": "20",
+    "57378e311c456719005744b2": "4",
+    "57378e311c456719005744b3": "Maxwell",
+    "573792ee1c456719005744b9": "electromagnetic theory",
+    "573792ee1c456719005744ba": "quantum mechanics",
+    "573792ee1c456719005744bb": "quantum electrodynamics",
+    "573792ee1c456719005744bc": "photons",
+    "573792ee1c456719005744bd": "quantum electrodynamics",
+    "5737958ac3c5551400e51f29": "the repulsion of like charges under the influence of the electromagnetic force",
+    "5737958ac3c5551400e51f2a": "repulsion of like charges under the influence of the electromagnetic force",
+    "5737958ac3c5551400e51f2b": "energy",
+    "5737958ac3c5551400e51f2c": "a structural force",
+    "5737958b1c456719005744c3": "the repulsion of like charges under the influence of the electromagnetic force",
+    "5737958b1c456719005744c4": "repulsion of like charges under the influence of the electromagnetic force",
+    "5737958b1c456719005744c5": "energy",
+    "5737958b1c456719005744c6": "a structural force",
+    "573796edc3c5551400e51f33": "elementary particles",
+    "573796edc3c5551400e51f34": "a residual of the force",
+    "573796edc3c5551400e51f35": "nuclear force",
+    "573796edc3c5551400e51f36": "gluons",
+    "573796edc3c5551400e51f37": "color confinement",
+    "57379829c3c5551400e51f3d": "The weak force",
+    "57379829c3c5551400e51f3e": "The weak force",
+    "57379829c3c5551400e51f3f": "neutrons in atomic nuclei",
+    "57379829c3c5551400e51f40": "1013",
+    "57379829c3c5551400e51f41": "1015 kelvins",
+    "57379a4b1c456719005744cd": "The normal force",
+    "57379a4b1c456719005744ce": "Pauli repulsion",
+    "57379a4b1c456719005744cf": "fermionic nature of electrons",
+    "57379a4b1c456719005744d0": "The normal force",
+    "57379ed81c456719005744d5": "ideal strings",
+    "57379ed81c456719005744d6": "ideal pulleys",
+    "57379ed81c456719005744d7": "instantaneously in action-reaction pairs",
+    "57379ed81c456719005744d8": "conservation of mechanical energy",
+    "57379ed81c456719005744d9": "connecting the same string multiple times to the same object through the use of a set-up that uses movable pulleys",
+    "5737a0acc3c5551400e51f47": "idealized point particles",
+    "5737a0acc3c5551400e51f48": "forces affect idealized point particles rather than three-dimensional objects",
+    "5737a0acc3c5551400e51f49": "extended",
+    "5737a0acc3c5551400e51f4a": "other parts of an object",
+    "5737a0acc3c5551400e51f4b": "extended structure",
+    "5737a25ac3c5551400e51f51": "The stress tensor",
+    "5737a25ac3c5551400e51f52": "stress-tensor",
+    "5737a25ac3c5551400e51f53": "pressure terms",
+    "5737a25ac3c5551400e51f54": "formalism",
+    "5737a4511c456719005744df": "angle",
+    "5737a4511c456719005744e0": "an unbalanced torque",
+    "5737a4511c456719005744e1": "Newton's Second Law of Motion",
+    "5737a5931c456719005744e7": "the center of the curving path",
+    "5737a5931c456719005744e8": "perpendicular",
+    "5737a5931c456719005744e9": "radial (centripetal) force",
+    "5737a5931c456719005744ea": "radial",
+    "5737a5931c456719005744eb": "tangential force",
+    "5737a7351c456719005744f1": "kinetic",
+    "5737a7351c456719005744f2": "A conservative force",
+    "5737a7351c456719005744f3": "net mechanical energy",
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\ No newline at end of file
diff --git a/hpo-examples/question-answering/qa/tokenizer_config.json b/hpo-examples/question-answering/qa/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd521218cd6f600b92f46dcff0561782cc34a958
--- /dev/null
+++ b/hpo-examples/question-answering/qa/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/hpo-examples/question-answering/qa/train_results.json b/hpo-examples/question-answering/qa/train_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..33f13ea7ea6fd6c4bd906dd2f8b86ba7f3d0ddd0
--- /dev/null
+++ b/hpo-examples/question-answering/qa/train_results.json
@@ -0,0 +1,9 @@
+{
+    "epoch": 2.0,
+    "total_flos": 3.541929151120589e+16,
+    "train_loss": 1.148573803161563,
+    "train_runtime": 3245.3985,
+    "train_samples": 88524,
+    "train_samples_per_second": 54.554,
+    "train_steps_per_second": 4.546
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\ No newline at end of file
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diff --git a/hpo-examples/question-answering/requirements.txt b/hpo-examples/question-answering/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c8200d867ec4f79ebb3c2649a5aa3773d0b7eb54
--- /dev/null
+++ b/hpo-examples/question-answering/requirements.txt
@@ -0,0 +1,4 @@
+accelerate >= 0.12.0
+datasets >= 1.8.0
+torch >= 1.3.0
+evaluate
\ No newline at end of file
diff --git a/hpo-examples/question-answering/run.sh b/hpo-examples/question-answering/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..286779bd9c65f263bcd1294a38f850165fa570a9
--- /dev/null
+++ b/hpo-examples/question-answering/run.sh
@@ -0,0 +1,15 @@
+
+# f1/exact_match = 88.52/81.22 -> 81.5137/88.6696
+CUDA_VISIBLE_DEVICES=0 python run_qa.py \
+  --model_name_or_path google-bert/bert-base-uncased \
+  --dataset_name squad \
+  --do_train \
+  --do_eval \
+  --per_device_train_batch_size 12 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 2 \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --output_dir ./baseline \
+  --overwrite_output_dir \
+  --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
diff --git a/hpo-examples/question-answering/run_qa.py b/hpo-examples/question-answering/run_qa.py
new file mode 100644
index 0000000000000000000000000000000000000000..c46fd2e3a82b66773bd0bbbc7ed0c495f245de41
--- /dev/null
+++ b/hpo-examples/question-answering/run_qa.py
@@ -0,0 +1,743 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for question answering using a slightly adapted version of the 🤗 Trainer.
+"""
+# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Optional, List
+
+import datasets
+import evaluate
+from datasets import load_dataset
+from trainer_qa import QuestionAnsweringTrainer
+from utils_qa import postprocess_qa_predictions
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForQuestionAnswering,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PreTrainedTokenizerFast,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+from trplib import apply_trp
+
+
+# # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+# check_min_version("4.50.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+
+    apply_trp: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to apply TRP or not."},
+    )
+    trp_depths: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "TRP depth value."
+        },
+    )
+    trp_p: Optional[float] = field(
+        default=0.1,
+        metadata={
+            "help": "TRP p value."
+        },
+    )
+    trp_lambdas: Optional[List[float]] = field(
+        default_factory=lambda: [0.4, 0.2, 0.1],
+        metadata={
+            "help": "TRP lambda values (list of floats)."
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    test_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input test data file to evaluate the perplexity on (a text file)."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_seq_length: int = field(
+        default=384,
+        metadata={
+            "help": (
+                "The maximum total input sequence length after tokenization. Sequences longer "
+                "than this will be truncated, sequences shorter will be padded."
+            )
+        },
+    )
+    pad_to_max_length: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether to pad all samples to `max_seq_length`. If False, will pad the samples dynamically when"
+                " batching to the maximum length in the batch (which can be faster on GPU but will be slower on TPU)."
+            )
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+                "value if set."
+            )
+        },
+    )
+    version_2_with_negative: bool = field(
+        default=False, metadata={"help": "If true, some of the examples do not have an answer."}
+    )
+    null_score_diff_threshold: float = field(
+        default=0.0,
+        metadata={
+            "help": (
+                "The threshold used to select the null answer: if the best answer has a score that is less than "
+                "the score of the null answer minus this threshold, the null answer is selected for this example. "
+                "Only useful when `version_2_with_negative=True`."
+            )
+        },
+    )
+    doc_stride: int = field(
+        default=128,
+        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
+    )
+    n_best_size: int = field(
+        default=20,
+        metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
+    )
+    max_answer_length: int = field(
+        default=30,
+        metadata={
+            "help": (
+                "The maximum length of an answer that can be generated. This is needed because the start "
+                "and end predictions are not conditioned on one another."
+            )
+        },
+    )
+
+    def __post_init__(self):
+        if (
+            self.dataset_name is None
+            and self.train_file is None
+            and self.validation_file is None
+            and self.test_file is None
+        ):
+            raise ValueError("Need either a dataset name or a training/validation file/test_file.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
+            if self.test_file is not None:
+                extension = self.test_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_qa", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
+        )
+    else:
+        data_files = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
+
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+            extension = data_args.validation_file.split(".")[-1]
+        if data_args.test_file is not None:
+            data_files["test"] = data_args.test_file
+            extension = data_args.test_file.split(".")[-1]
+        raw_datasets = load_dataset(
+            extension,
+            data_files=data_files,
+            field="data",
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+        )
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.
+
+    # Load pretrained model and tokenizer
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=True,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    model = AutoModelForQuestionAnswering.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    if model_args.apply_trp:
+        model = apply_trp(model, model_args.trp_depths, model_args.trp_p, model_args.trp_lambdas)
+
+    # Tokenizer check: this script requires a fast tokenizer.
+    if not isinstance(tokenizer, PreTrainedTokenizerFast):
+        raise ValueError(
+            "This example script only works for models that have a fast tokenizer. Checkout the big table of models at"
+            " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
+            " this requirement"
+        )
+
+    # Preprocessing the datasets.
+    # Preprocessing is slightly different for training and evaluation.
+    if training_args.do_train:
+        column_names = raw_datasets["train"].column_names
+    elif training_args.do_eval:
+        column_names = raw_datasets["validation"].column_names
+    else:
+        column_names = raw_datasets["test"].column_names
+    question_column_name = "question" if "question" in column_names else column_names[0]
+    context_column_name = "context" if "context" in column_names else column_names[1]
+    answer_column_name = "answers" if "answers" in column_names else column_names[2]
+
+    # Padding side determines if we do (question|context) or (context|question).
+    pad_on_right = tokenizer.padding_side == "right"
+
+    if data_args.max_seq_length > tokenizer.model_max_length:
+        logger.warning(
+            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
+            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+        )
+    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
+    # Training preprocessing
+    def prepare_train_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
+        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
+        # in one example possible giving several features when a context is long, each of those features having a
+        # context that overlaps a bit the context of the previous feature.
+        tokenized_examples = tokenizer(
+            examples[question_column_name if pad_on_right else context_column_name],
+            examples[context_column_name if pad_on_right else question_column_name],
+            truncation="only_second" if pad_on_right else "only_first",
+            max_length=max_seq_length,
+            stride=data_args.doc_stride,
+            return_overflowing_tokens=True,
+            return_offsets_mapping=True,
+            padding="max_length" if data_args.pad_to_max_length else False,
+        )
+
+        # Since one example might give us several features if it has a long context, we need a map from a feature to
+        # its corresponding example. This key gives us just that.
+        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
+        # The offset mappings will give us a map from token to character position in the original context. This will
+        # help us compute the start_positions and end_positions.
+        offset_mapping = tokenized_examples.pop("offset_mapping")
+
+        # Let's label those examples!
+        tokenized_examples["start_positions"] = []
+        tokenized_examples["end_positions"] = []
+
+        for i, offsets in enumerate(offset_mapping):
+            # We will label impossible answers with the index of the CLS token.
+            input_ids = tokenized_examples["input_ids"][i]
+            if tokenizer.cls_token_id in input_ids:
+                cls_index = input_ids.index(tokenizer.cls_token_id)
+            elif tokenizer.bos_token_id in input_ids:
+                cls_index = input_ids.index(tokenizer.bos_token_id)
+            else:
+                cls_index = 0
+
+            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
+            sequence_ids = tokenized_examples.sequence_ids(i)
+
+            # One example can give several spans, this is the index of the example containing this span of text.
+            sample_index = sample_mapping[i]
+            answers = examples[answer_column_name][sample_index]
+            # If no answers are given, set the cls_index as answer.
+            if len(answers["answer_start"]) == 0:
+                tokenized_examples["start_positions"].append(cls_index)
+                tokenized_examples["end_positions"].append(cls_index)
+            else:
+                # Start/end character index of the answer in the text.
+                start_char = answers["answer_start"][0]
+                end_char = start_char + len(answers["text"][0])
+
+                # Start token index of the current span in the text.
+                token_start_index = 0
+                while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
+                    token_start_index += 1
+
+                # End token index of the current span in the text.
+                token_end_index = len(input_ids) - 1
+                while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
+                    token_end_index -= 1
+
+                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
+                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
+                    tokenized_examples["start_positions"].append(cls_index)
+                    tokenized_examples["end_positions"].append(cls_index)
+                else:
+                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
+                    # Note: we could go after the last offset if the answer is the last word (edge case).
+                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
+                        token_start_index += 1
+                    tokenized_examples["start_positions"].append(token_start_index - 1)
+                    while offsets[token_end_index][1] >= end_char:
+                        token_end_index -= 1
+                    tokenized_examples["end_positions"].append(token_end_index + 1)
+
+        return tokenized_examples
+
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            # We will select sample from whole data if argument is specified
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+        # Create train feature from dataset
+        with training_args.main_process_first(desc="train dataset map pre-processing"):
+            train_dataset = train_dataset.map(
+                prepare_train_features,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on train dataset",
+            )
+        if data_args.max_train_samples is not None:
+            # Number of samples might increase during Feature Creation, We select only specified max samples
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+
+    # Validation preprocessing
+    def prepare_validation_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
+        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
+        # in one example possible giving several features when a context is long, each of those features having a
+        # context that overlaps a bit the context of the previous feature.
+        tokenized_examples = tokenizer(
+            examples[question_column_name if pad_on_right else context_column_name],
+            examples[context_column_name if pad_on_right else question_column_name],
+            truncation="only_second" if pad_on_right else "only_first",
+            max_length=max_seq_length,
+            stride=data_args.doc_stride,
+            return_overflowing_tokens=True,
+            return_offsets_mapping=True,
+            padding="max_length" if data_args.pad_to_max_length else False,
+        )
+
+        # Since one example might give us several features if it has a long context, we need a map from a feature to
+        # its corresponding example. This key gives us just that.
+        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
+
+        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
+        # corresponding example_id and we will store the offset mappings.
+        tokenized_examples["example_id"] = []
+
+        for i in range(len(tokenized_examples["input_ids"])):
+            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
+            sequence_ids = tokenized_examples.sequence_ids(i)
+            context_index = 1 if pad_on_right else 0
+
+            # One example can give several spans, this is the index of the example containing this span of text.
+            sample_index = sample_mapping[i]
+            tokenized_examples["example_id"].append(examples["id"][sample_index])
+
+            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
+            # position is part of the context or not.
+            tokenized_examples["offset_mapping"][i] = [
+                (o if sequence_ids[k] == context_index else None)
+                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
+            ]
+
+        return tokenized_examples
+
+    if training_args.do_eval:
+        if "validation" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_examples = raw_datasets["validation"]
+        if data_args.max_eval_samples is not None:
+            # We will select sample from whole data
+            max_eval_samples = min(len(eval_examples), data_args.max_eval_samples)
+            eval_examples = eval_examples.select(range(max_eval_samples))
+        # Validation Feature Creation
+        with training_args.main_process_first(desc="validation dataset map pre-processing"):
+            eval_dataset = eval_examples.map(
+                prepare_validation_features,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on validation dataset",
+            )
+        if data_args.max_eval_samples is not None:
+            # During Feature creation dataset samples might increase, we will select required samples again
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+
+    if training_args.do_predict:
+        if "test" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_examples = raw_datasets["test"]
+        if data_args.max_predict_samples is not None:
+            # We will select sample from whole data
+            predict_examples = predict_examples.select(range(data_args.max_predict_samples))
+        # Predict Feature Creation
+        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
+            predict_dataset = predict_examples.map(
+                prepare_validation_features,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on prediction dataset",
+            )
+        if data_args.max_predict_samples is not None:
+            # During Feature creation dataset samples might increase, we will select required samples again
+            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
+            predict_dataset = predict_dataset.select(range(max_predict_samples))
+
+    # Data collator
+    # We have already padded to max length if the corresponding flag is True, otherwise we need to pad in the data
+    # collator.
+    data_collator = (
+        default_data_collator
+        if data_args.pad_to_max_length
+        else DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
+    )
+
+    # Post-processing:
+    def post_processing_function(examples, features, predictions, stage="eval"):
+        # Post-processing: we match the start logits and end logits to answers in the original context.
+        predictions = postprocess_qa_predictions(
+            examples=examples,
+            features=features,
+            predictions=predictions,
+            version_2_with_negative=data_args.version_2_with_negative,
+            n_best_size=data_args.n_best_size,
+            max_answer_length=data_args.max_answer_length,
+            null_score_diff_threshold=data_args.null_score_diff_threshold,
+            output_dir=training_args.output_dir,
+            log_level=log_level,
+            prefix=stage,
+        )
+        # Format the result to the format the metric expects.
+        if data_args.version_2_with_negative:
+            formatted_predictions = [
+                {"id": str(k), "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
+            ]
+        else:
+            formatted_predictions = [{"id": str(k), "prediction_text": v} for k, v in predictions.items()]
+
+        references = [{"id": str(ex["id"]), "answers": ex[answer_column_name]} for ex in examples]
+        return EvalPrediction(predictions=formatted_predictions, label_ids=references)
+
+    if data_args.version_2_with_negative:
+        accepted_best_metrics = ("exact", "f1", "HasAns_exact", "HasAns_f1")
+    else:
+        accepted_best_metrics = ("exact_match", "f1")
+
+    if training_args.load_best_model_at_end and training_args.metric_for_best_model not in accepted_best_metrics:
+        warnings.warn(f"--metric_for_best_model should be set to one of {accepted_best_metrics}")
+
+    metric = evaluate.load(
+        "squad_v2" if data_args.version_2_with_negative else "squad", cache_dir=model_args.cache_dir
+    )
+
+    def compute_metrics(p: EvalPrediction):
+        return metric.compute(predictions=p.predictions, references=p.label_ids)
+
+    # Initialize our Trainer
+    trainer = QuestionAnsweringTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        eval_examples=eval_examples if training_args.do_eval else None,
+        processing_class=tokenizer,
+        data_collator=data_collator,
+        post_process_function=post_processing_function,
+        compute_metrics=compute_metrics,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+
+        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Prediction
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+        results = trainer.predict(predict_dataset, predict_examples)
+        metrics = results.metrics
+
+        max_predict_samples = (
+            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
+        )
+        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
+
+        trainer.log_metrics("predict", metrics)
+        trainer.save_metrics("predict", metrics)
+
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hpo-examples/question-answering/trainer_qa.py b/hpo-examples/question-answering/trainer_qa.py
new file mode 100644
index 0000000000000000000000000000000000000000..10428a2b77e5dd6fff03aabbbbd24fdada6dc2c4
--- /dev/null
+++ b/hpo-examples/question-answering/trainer_qa.py
@@ -0,0 +1,137 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+A subclass of `Trainer` specific to Question-Answering tasks
+"""
+
+import math
+import time
+
+from transformers import Trainer, is_torch_xla_available
+from transformers.trainer_utils import PredictionOutput, speed_metrics
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+    import torch_xla.debug.metrics as met
+
+
+class QuestionAnsweringTrainer(Trainer):
+    def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.eval_examples = eval_examples
+        self.post_process_function = post_process_function
+
+    def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"):
+        eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
+        eval_dataloader = self.get_eval_dataloader(eval_dataset)
+        eval_examples = self.eval_examples if eval_examples is None else eval_examples
+
+        # Temporarily disable metric computation, we will do it in the loop here.
+        compute_metrics = self.compute_metrics
+        self.compute_metrics = None
+        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
+        start_time = time.time()
+        try:
+            output = eval_loop(
+                eval_dataloader,
+                description="Evaluation",
+                # No point gathering the predictions if there are no metrics, otherwise we defer to
+                # self.args.prediction_loss_only
+                prediction_loss_only=True if compute_metrics is None else None,
+                ignore_keys=ignore_keys,
+                metric_key_prefix=metric_key_prefix,
+            )
+        finally:
+            self.compute_metrics = compute_metrics
+        total_batch_size = self.args.eval_batch_size * self.args.world_size
+        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
+            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
+        output.metrics.update(
+            speed_metrics(
+                metric_key_prefix,
+                start_time,
+                num_samples=output.num_samples,
+                num_steps=math.ceil(output.num_samples / total_batch_size),
+            )
+        )
+        if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
+            # Only the main node write the results by default
+            eval_preds = self.post_process_function(eval_examples, eval_dataset, output.predictions)
+            metrics = self.compute_metrics(eval_preds)
+
+            # Prefix all keys with metric_key_prefix + '_'
+            for key in list(metrics.keys()):
+                if not key.startswith(f"{metric_key_prefix}_"):
+                    metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
+            metrics.update(output.metrics)
+        else:
+            metrics = output.metrics
+
+        if self.args.should_log:
+            # Only the main node log the results by default
+            self.log(metrics)
+
+        if self.args.tpu_metrics_debug or self.args.debug:
+            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
+            xm.master_print(met.metrics_report())
+
+        self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
+        return metrics
+
+    def predict(self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test"):
+        predict_dataloader = self.get_test_dataloader(predict_dataset)
+
+        # Temporarily disable metric computation, we will do it in the loop here.
+        compute_metrics = self.compute_metrics
+        self.compute_metrics = None
+        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
+        start_time = time.time()
+        try:
+            output = eval_loop(
+                predict_dataloader,
+                description="Prediction",
+                # No point gathering the predictions if there are no metrics, otherwise we defer to
+                # self.args.prediction_loss_only
+                prediction_loss_only=True if compute_metrics is None else None,
+                ignore_keys=ignore_keys,
+                metric_key_prefix=metric_key_prefix,
+            )
+        finally:
+            self.compute_metrics = compute_metrics
+        total_batch_size = self.args.eval_batch_size * self.args.world_size
+        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
+            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
+        output.metrics.update(
+            speed_metrics(
+                metric_key_prefix,
+                start_time,
+                num_samples=output.num_samples,
+                num_steps=math.ceil(output.num_samples / total_batch_size),
+            )
+        )
+
+        if self.post_process_function is None or self.compute_metrics is None:
+            return output
+
+        predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict")
+        metrics = self.compute_metrics(predictions)
+
+        # Prefix all keys with metric_key_prefix + '_'
+        for key in list(metrics.keys()):
+            if not key.startswith(f"{metric_key_prefix}_"):
+                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
+        metrics.update(output.metrics)
+        return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics)
diff --git a/hpo-examples/question-answering/trplib.py b/hpo-examples/question-answering/trplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0fb33879b1ab05f53e0b8e7068729abbb96146
--- /dev/null
+++ b/hpo-examples/question-answering/trplib.py
@@ -0,0 +1,1181 @@
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from torchvision.models.mobilenetv2 import MobileNetV2
+from torchvision.models.resnet import ResNet
+from torchvision.models.efficientnet import EfficientNet
+from torchvision.models.vision_transformer import VisionTransformer
+from torchvision.models.segmentation.fcn import FCN
+from torchvision.models.segmentation.deeplabv3 import DeepLabV3
+
+import transformers
+from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, CausalLMOutput, Seq2SeqLMOutput
+
+from typing import Optional, Tuple, List, Union, Callable
+from collections import OrderedDict
+import types
+
+
+def trp_criterion(trp_blocks: nn.ModuleList, shared_head: Callable, criterion: Callable, lambdas: List[float], hidden_states: Tensor, logits: Tensor, targets: Tensor, loss_normalization=False):
+    loss, mask = criterion(logits, targets)
+    if loss_normalization:
+        coeff = loss.detach()
+
+    embeds = [hidden_states]
+    predictions = []
+    for k, c in enumerate(lambdas):
+        embeds.append(trp_blocks[k](embeds[-1]))
+        predictions.append(shared_head(embeds[-1]))
+        replica_loss, mask = criterion(predictions[-1], targets, mask)
+        loss += c * replica_loss
+    
+    if loss_normalization:
+        with torch.no_grad():
+            coeff = torch.exp(coeff) / torch.exp(loss.detach())
+        loss = coeff * loss
+    
+    return loss
+
+
+class TPBlock(nn.Module):
+    def __init__(self, depths: int, in_features: int, p: float, dim=-1):
+        super(TPBlock, self).__init__()
+
+        self.dropout = nn.Dropout(p)
+
+        self.cdim = dim
+
+        blocks = []
+        for _ in range(depths):
+            blocks.append(nn.Linear(in_features, in_features))
+            nn.init.constant_(blocks[-1].weight, 0.0)
+            nn.init.constant_(blocks[-1].bias, 0.0)
+            blocks.append(nn.ReLU())
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        x = self.dropout(x)
+        if self.cdim == -1:
+            x = x + self.blocks(x)
+        else:
+            x = x + torch.movedim(self.blocks(torch.movedim(x, self.cdim, -1)), -1, self.cdim)
+        return x
+    
+
+class Config:
+    @staticmethod
+    def gen_criterion(*args, **kwargs):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor.
+                target (Tensor): Target labels.
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor with the same shape of target.
+            """
+            pass
+        return func
+    
+    @staticmethod
+    def gen_shared_head(*args, **kwargs):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States tensor.
+
+            Returns:
+                logits (Tensor): Logits tensor.
+            """
+            pass
+        return func
+
+    @staticmethod
+    def forward(*args, **kwargs):
+        pass
+
+
+# Wav2Vec2 for Audio Classification
+class Wav2Vec2ForSequenceClassificationConfig(Config): 
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self, attention_mask):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+            output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            if self.config.use_weighted_layer_sum:
+                hidden_states = outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION]
+                hidden_states = torch.stack(hidden_states, dim=1)
+                norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+                hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+            else:
+                hidden_states = outputs[0]
+
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                shared_head = Wav2Vec2ForSequenceClassificationConfig.gen_shared_head(self, attention_mask)
+                criterion = Wav2Vec2ForSequenceClassificationConfig.gen_criterion()
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states, logits.view(-1, self.config.num_labels),  labels.view(-1), loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# MobileNetV2 for Image Classification
+class MobileNetV2Config(Config):
+    @staticmethod
+    def gen_criterion(label_smoothing=0.0, top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B] or [B, C].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            label = torch.argmax(target, dim=1) if label_smoothing > 0.0 else target
+                
+            unmasked_loss = F.cross_entropy(input, label, reduction="none", label_smoothing=label_smoothing)
+            if mask is None:
+                mask = torch.ones_like(unmasked_loss, dtype=torch.float32, device=target.device)
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=-1)
+                mask = mask * torch.eq(topk_indices, label[:, None]).any(dim=-1).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = MobileNetV2Config.gen_shared_head(self)
+                criterion = MobileNetV2Config.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ResNet for Image Classification
+class ResNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.fc(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.conv1(images)
+            x = self.bn1(x)
+            x = self.relu(x)
+            x = self.maxpool(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.fc(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = ResNetConfig.gen_shared_head(self)
+                criterion = ResNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# EfficientNet for Image Classification
+class EfficientNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = EfficientNetConfig.gen_shared_head(self)
+                criterion = EfficientNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ViT for Image Classification
+class VisionTransformerConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.heads(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self._process_input(images)
+            n = x.shape[0]
+            batch_class_token = self.class_token.expand(n, -1, -1)
+            x = torch.cat([batch_class_token, x], dim=1)
+            x = self.encoder(x)
+            x = x[:, 0]
+
+            logits = self.heads(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = VisionTransformerConfig.gen_shared_head(self)
+                criterion = VisionTransformerConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# Bert for Question Answering
+class BertForQuestionAnsweringConfig(Config): 
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target: List[Tensor], mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C, 2].
+                target (List[Tensor]): 
+                    Start Positions of shape [B].
+                    End Positions of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            start_positions, end_positions = target
+
+            if mask is None:
+                mask = torch.ones_like(start_positions, dtype=torch.float32, device=start_positions.device)
+
+            start_logits, end_logits = input.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            masked_start_losses = F.cross_entropy(start_logits, start_positions, ignore_index=ignored_index, reduction="none")
+            start_loss = torch.sum(mask * masked_start_losses) / (torch.sum(mask) + 1e-6)
+            masked_end_losses = F.cross_entropy(end_logits, end_positions, ignore_index=ignored_index, reduction="none")
+            end_loss = torch.sum(mask * masked_end_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(start_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, start_positions[:, None]).any(dim=1).to(start_logits.dtype)
+                topk_values, topk_indices = torch.topk(end_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, end_positions[:, None]).any(dim=1).to(end_logits.dtype)
+
+            return (start_loss + end_loss) / 2, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.qa_outputs(hidden_states)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            start_positions: Optional[torch.Tensor] = None,
+            end_positions: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+            r"""
+            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the start of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the end of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            sequence_output = outputs[0]
+
+            logits = self.qa_outputs(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                shared_head = BertForQuestionAnsweringConfig.gen_shared_head(self)
+                criterion = BertForQuestionAnsweringConfig.gen_criterion()
+                total_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, [start_positions, end_positions], loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+    
+
+# FCN for Semantic Segmentation
+class FCNConfig(Config):
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input Tensor: input tensor of shape [B, C, H, W].
+                target (Tensor): Target labels of shape [B, H, W].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B, H, W].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+                
+            masked_loss = F.cross_entropy(input, target, ignore_index=255, reduction="none")
+            loss = torch.sum(mask * masked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, target[:, None, :, :]).any(dim=1).to(input.dtype)
+                # mask = mask * torch.eq(torch.argmax(x, dim=1), target).to(x.dtype)
+                
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_out_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+    
+    @staticmethod
+    def gen_aux_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.aux_classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            input_shape = images.shape[-2:]
+            # contract: features is a dict of tensors
+            features = self.backbone(images)
+
+            result = OrderedDict()
+            x = features["out"]
+            x = self.classifier(x)
+            x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            result["out"] = x
+
+            if self.aux_classifier is not None:
+                x = features["aux"]
+                x = self.aux_classifier(x)
+                x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+                result["aux"] = x
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                out_shared_head = FCNConfig.gen_out_shared_head(self, input_shape)
+                aux_shared_head = FCNConfig.gen_aux_shared_head(self, input_shape)
+                criterion = FCNConfig.gen_criterion(top_k)
+                out_loss = trp_criterion(self.out_trp_blocks, out_shared_head, criterion, lambdas, features["out"], result["out"], targets, loss_normalization)
+                aux_loss = trp_criterion(self.aux_trp_blocks, aux_shared_head, criterion, lambdas, features["aux"], result["aux"], targets, loss_normalization)
+                loss = out_loss + 0.5 * aux_loss
+                return result, loss
+            return result
+        return func
+
+
+# DeepLabV3Config for Semantic Segmentation
+class DeepLabV3Config(FCNConfig):
+    pass
+
+
+# Bert for Text Classification
+class BertForSequenceClassificationConfig(Config): 
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, pooled_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Boberta for Text Classification
+class RobertaForSequenceClassificationConfig(BertForSequenceClassificationConfig): 
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.roberta(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            sequence_output = outputs[0]
+            logits = self.classifier(sequence_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                # move labels to correct device to enable model parallelism
+                labels = labels.to(logits.device)
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Wav2Vec2 for Speech Recognition
+class Wav2Vec2ForCTCConfig(Config):
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def greedy_decode_ctc(
+        log_probs: torch.Tensor,
+        input_lengths: torch.Tensor,
+        blank_token_id: int,
+        target_lengths: torch.Tensor
+    ):
+        """
+        Convert logits to flattened predictions that match the shape of flattened_targets.
+
+        Args:
+            log_probs: [B, L, V] - log-softmax output
+            input_lengths: [B] - actual length of each input
+            blank_token_id: int - index of blank token
+            target_lengths: [B] - used to determine how many predictions to keep per sample
+
+        Returns:
+            flattened_predictions: 1D tensor, same total length as sum(target_lengths)
+        """
+        batch_size = log_probs.size(0)
+        decoded_all = []
+
+        predicted_ids = log_probs.argmax(dim=-1)  # [B, L]
+
+        for i in range(batch_size):
+            pred = predicted_ids[i][:input_lengths[i]]  # [Li]
+            prev = None
+            decoded = []
+            for token in pred:
+                token = token.item()
+                if token != blank_token_id and token != prev:
+                    decoded.append(token)
+                prev = token
+            # Trim or pad to match target_lengths[i]
+            tgt_len = target_lengths[i].item()
+            if len(decoded) >= tgt_len:
+                decoded = decoded[:tgt_len]
+            else:
+                decoded = decoded + [blank_token_id] * (tgt_len - len(decoded))  # pad with blank
+            decoded_all.extend(decoded)
+
+        return torch.tensor(decoded_all, dtype=torch.long, device=log_probs.device)  # shape: [sum(target_lengths)]
+
+    @staticmethod
+    def gen_criterion(input_lengths: Tensor, pad_token_id: int, ctc_zero_infinity: bool):
+        def func(logits: Tensor, labels: Tensor, mask=None):
+            """
+            Args:
+                logits (Tensor): Log Probablities of shape [B, L, V].
+                labels (Tensor): Flattened Targets of shape [B, L'].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(input_lengths, dtype=torch.float32, device=input_lengths.device)
+
+            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            with torch.backends.cudnn.flags(enabled=False):
+                masked_losses = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=pad_token_id, reduction="none", zero_infinity=ctc_zero_infinity)
+                loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                thres = 0.5
+                flattened_predictions = Wav2Vec2ForCTCConfig.greedy_decode_ctc(
+                    log_probs.transpose(0, 1),  # [B, T, V]
+                    input_lengths=input_lengths,
+                    blank_token_id=pad_token_id,
+                    target_lengths=target_lengths
+                )
+                token_wise_mask =  torch.eq(flattened_predictions, flattened_targets).to(flattened_targets.dtype)
+                segment_ids = torch.arange(len(target_lengths), device=target_lengths.device).repeat_interleave(target_lengths)
+                sequence_wise_mask = torch.zeros(len(target_lengths), dtype=target_lengths.dtype, device=token_wise_mask.device).scatter_add(0, segment_ids, token_wise_mask)
+                mask = mask * torch.ge(sequence_wise_mask, thres * target_lengths).to(flattened_targets.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.lm_head(hidden_states)
+            # log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, CausalLMOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
+                Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
+                the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
+                All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
+                config.vocab_size - 1]`.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None and labels.max() >= self.config.vocab_size:
+                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            hidden_states = self.dropout(hidden_states)
+
+            logits = self.lm_head(hidden_states)
+
+            loss = None
+            if labels is not None:
+                # retrieve loss input_lengths from attention_mask
+                attention_mask = (
+                    attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
+                )
+                input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+                shared_head = Wav2Vec2ForCTCConfig.gen_shared_head(self)
+                criterion = Wav2Vec2ForCTCConfig.gen_criterion(input_lengths, self.config.pad_token_id, self.config.ctc_zero_infinity)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states,  logits, labels, loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForCTCConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return CausalLMOutput(
+                loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+            )
+        return func
+
+
+# MBart for Translation
+class MBartForConditionalGenerationConfig(Config): 
+    @staticmethod
+    def gen_criterion(vocab_size: int, top_k=1):
+        def func(logits, labels, mask=None):
+            """
+            Args:
+                logits (Tensor): Logits tensor of shape [B, L, V].
+                labels (Tensor): Target labels of shape [B, L].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(labels.view(-1), dtype=torch.float32, device=labels.device)
+                
+            masked_losses = F.cross_entropy(logits.view(-1, vocab_size), labels.view(-1), reduction="none")
+            loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(logits.view(-1, vocab_size), top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, labels.view(-1, 1)).any(dim=1).to(logits.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, L].
+            """
+            logits = self.lm_head(hidden_states) + self.final_logits_bias
+            return logits
+        return func
+    
+    @staticmethod
+    def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+        """
+        Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
+        have a single `decoder_start_token_id` in contrast to other Bart-like models.
+        """
+        prev_output_tokens = input_ids.clone()
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+        index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+        decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+        prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+        prev_output_tokens[:, 0] = decoder_start_tokens
+
+        return prev_output_tokens
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            Returns:
+
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None:
+                # if use_cache:
+                #     logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+                use_cache = False
+                if decoder_input_ids is None and decoder_inputs_embeds is None:
+                    decoder_input_ids = MBartForConditionalGenerationConfig.shift_tokens_right(labels, self.config.pad_token_id)
+
+            outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=decoder_input_ids,
+                encoder_outputs=encoder_outputs,
+                decoder_attention_mask=decoder_attention_mask,
+                head_mask=head_mask,
+                decoder_head_mask=decoder_head_mask,
+                cross_attn_head_mask=cross_attn_head_mask,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+            masked_lm_loss = None
+            if labels is not None:
+                shared_head = MBartForConditionalGenerationConfig.gen_shared_head(self)
+                criterion = MBartForConditionalGenerationConfig.gen_criterion(self.config.vocab_size)
+                masked_lm_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, outputs[0],  lm_logits, labels, loss_normalization)
+
+            if not return_dict:
+                output = (lm_logits,) + outputs[1:]
+                return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+            return Seq2SeqLMOutput(
+                loss=masked_lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                decoder_hidden_states=outputs.decoder_hidden_states,
+                decoder_attentions=outputs.decoder_attentions,
+                cross_attentions=outputs.cross_attentions,
+                encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+                encoder_hidden_states=outputs.encoder_hidden_states,
+                encoder_attentions=outputs.encoder_attentions,
+            )
+        return func
+
+
+def apply_trp(model, depths: int, p: float, lambdas: List[float], **kwargs):
+    if isinstance(model, transformers.Wav2Vec2ForSequenceClassification):
+        print("✅ Applying TRP to Wav2Vec2 for Audio Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, MobileNetV2):
+        print("✅ Applying TRP to MobileNetV2 for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(MobileNetV2Config.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, ResNet):
+        print("✅ Applying TRP to ResNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p) for _ in lambdas])
+        model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, EfficientNet):
+        print("✅ Applying TRP to EfficientNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(EfficientNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, VisionTransformer):
+        print("✅ Applying TRP to VisionTransformer for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(VisionTransformerConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, transformers.BertForQuestionAnswering):
+        print("✅ Applying TRP to Bert for Question Answering...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForQuestionAnsweringConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, FCN):
+        print("✅ Applying TRP to FCN for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(FCNConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, DeepLabV3):
+        print("✅ Applying TRP to DeepLabV3 for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(DeepLabV3Config.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, transformers.BertForSequenceClassification):
+        print("✅ Applying TRP to Bert for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.RobertaForSequenceClassification):
+        print("✅ Applying TRP to Roberta for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(RobertaForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.Wav2Vec2ForCTC):
+        print("✅ Applying TRP to Wav2Vec2 for Speech Recognition...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForCTCConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.MBartForConditionalGeneration):
+        print("✅ Applying TRP to MBart for Translation...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(MBartForConditionalGenerationConfig.gen_forward(lambdas, False), model)
+    else:
+        torch._assert(
+            isinstance(model, transformers.Wav2Vec2ForSequenceClassification), 
+            "The model should be an object of [`Wav2Vec2ForSequenceClassification`].")
+
+    return model
\ No newline at end of file
diff --git a/hpo-examples/question-answering/utils_qa.py b/hpo-examples/question-answering/utils_qa.py
new file mode 100644
index 0000000000000000000000000000000000000000..79497dbb816ed2bf3d33e131abecd1aa6caf6027
--- /dev/null
+++ b/hpo-examples/question-answering/utils_qa.py
@@ -0,0 +1,444 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Post-processing utilities for question answering.
+"""
+
+import collections
+import json
+import logging
+import os
+from typing import Optional, Tuple
+
+import numpy as np
+from tqdm.auto import tqdm
+
+
+logger = logging.getLogger(__name__)
+
+
+def postprocess_qa_predictions(
+    examples,
+    features,
+    predictions: Tuple[np.ndarray, np.ndarray],
+    version_2_with_negative: bool = False,
+    n_best_size: int = 20,
+    max_answer_length: int = 30,
+    null_score_diff_threshold: float = 0.0,
+    output_dir: Optional[str] = None,
+    prefix: Optional[str] = None,
+    log_level: Optional[int] = logging.WARNING,
+):
+    """
+    Post-processes the predictions of a question-answering model to convert them to answers that are substrings of the
+    original contexts. This is the base postprocessing functions for models that only return start and end logits.
+
+    Args:
+        examples: The non-preprocessed dataset (see the main script for more information).
+        features: The processed dataset (see the main script for more information).
+        predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
+            The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
+            first dimension must match the number of elements of :obj:`features`.
+        version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not the underlying dataset contains examples with no answers.
+        n_best_size (:obj:`int`, `optional`, defaults to 20):
+            The total number of n-best predictions to generate when looking for an answer.
+        max_answer_length (:obj:`int`, `optional`, defaults to 30):
+            The maximum length of an answer that can be generated. This is needed because the start and end predictions
+            are not conditioned on one another.
+        null_score_diff_threshold (:obj:`float`, `optional`, defaults to 0):
+            The threshold used to select the null answer: if the best answer has a score that is less than the score of
+            the null answer minus this threshold, the null answer is selected for this example (note that the score of
+            the null answer for an example giving several features is the minimum of the scores for the null answer on
+            each feature: all features must be aligned on the fact they `want` to predict a null answer).
+
+            Only useful when :obj:`version_2_with_negative` is :obj:`True`.
+        output_dir (:obj:`str`, `optional`):
+            If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
+            :obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
+            answers, are saved in `output_dir`.
+        prefix (:obj:`str`, `optional`):
+            If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
+        log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
+            ``logging`` log level (e.g., ``logging.WARNING``)
+    """
+    if len(predictions) != 2:
+        raise ValueError("`predictions` should be a tuple with two elements (start_logits, end_logits).")
+    all_start_logits, all_end_logits = predictions
+
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
+
+    # Build a map example to its corresponding features.
+    example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
+    features_per_example = collections.defaultdict(list)
+    for i, feature in enumerate(features):
+        features_per_example[example_id_to_index[feature["example_id"]]].append(i)
+
+    # The dictionaries we have to fill.
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    if version_2_with_negative:
+        scores_diff_json = collections.OrderedDict()
+
+    # Logging.
+    logger.setLevel(log_level)
+    logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
+
+    # Let's loop over all the examples!
+    for example_index, example in enumerate(tqdm(examples)):
+        # Those are the indices of the features associated to the current example.
+        feature_indices = features_per_example[example_index]
+
+        min_null_prediction = None
+        prelim_predictions = []
+
+        # Looping through all the features associated to the current example.
+        for feature_index in feature_indices:
+            # We grab the predictions of the model for this feature.
+            start_logits = all_start_logits[feature_index]
+            end_logits = all_end_logits[feature_index]
+            # This is what will allow us to map some the positions in our logits to span of texts in the original
+            # context.
+            offset_mapping = features[feature_index]["offset_mapping"]
+            # Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
+            # available in the current feature.
+            token_is_max_context = features[feature_index].get("token_is_max_context", None)
+
+            # Update minimum null prediction.
+            feature_null_score = start_logits[0] + end_logits[0]
+            if min_null_prediction is None or min_null_prediction["score"] > feature_null_score:
+                min_null_prediction = {
+                    "offsets": (0, 0),
+                    "score": feature_null_score,
+                    "start_logit": start_logits[0],
+                    "end_logit": end_logits[0],
+                }
+
+            # Go through all possibilities for the `n_best_size` greater start and end logits.
+            start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
+            end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
+                    # to part of the input_ids that are not in the context.
+                    if (
+                        start_index >= len(offset_mapping)
+                        or end_index >= len(offset_mapping)
+                        or offset_mapping[start_index] is None
+                        or len(offset_mapping[start_index]) < 2
+                        or offset_mapping[end_index] is None
+                        or len(offset_mapping[end_index]) < 2
+                    ):
+                        continue
+                    # Don't consider answers with a length that is either < 0 or > max_answer_length.
+                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:
+                        continue
+                    # Don't consider answer that don't have the maximum context available (if such information is
+                    # provided).
+                    if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
+                        continue
+
+                    prelim_predictions.append(
+                        {
+                            "offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
+                            "score": start_logits[start_index] + end_logits[end_index],
+                            "start_logit": start_logits[start_index],
+                            "end_logit": end_logits[end_index],
+                        }
+                    )
+        if version_2_with_negative and min_null_prediction is not None:
+            # Add the minimum null prediction
+            prelim_predictions.append(min_null_prediction)
+            null_score = min_null_prediction["score"]
+
+        # Only keep the best `n_best_size` predictions.
+        predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
+
+        # Add back the minimum null prediction if it was removed because of its low score.
+        if (
+            version_2_with_negative
+            and min_null_prediction is not None
+            and not any(p["offsets"] == (0, 0) for p in predictions)
+        ):
+            predictions.append(min_null_prediction)
+
+        # Use the offsets to gather the answer text in the original context.
+        context = example["context"]
+        for pred in predictions:
+            offsets = pred.pop("offsets")
+            pred["text"] = context[offsets[0] : offsets[1]]
+
+        # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
+        # failure.
+        if len(predictions) == 0 or (len(predictions) == 1 and predictions[0]["text"] == ""):
+            predictions.insert(0, {"text": "empty", "start_logit": 0.0, "end_logit": 0.0, "score": 0.0})
+
+        # Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
+        # the LogSumExp trick).
+        scores = np.array([pred.pop("score") for pred in predictions])
+        exp_scores = np.exp(scores - np.max(scores))
+        probs = exp_scores / exp_scores.sum()
+
+        # Include the probabilities in our predictions.
+        for prob, pred in zip(probs, predictions):
+            pred["probability"] = prob
+
+        # Pick the best prediction. If the null answer is not possible, this is easy.
+        if not version_2_with_negative:
+            all_predictions[example["id"]] = predictions[0]["text"]
+        else:
+            # Otherwise we first need to find the best non-empty prediction.
+            i = 0
+            while predictions[i]["text"] == "":
+                i += 1
+            best_non_null_pred = predictions[i]
+
+            # Then we compare to the null prediction using the threshold.
+            score_diff = null_score - best_non_null_pred["start_logit"] - best_non_null_pred["end_logit"]
+            scores_diff_json[example["id"]] = float(score_diff)  # To be JSON-serializable.
+            if score_diff > null_score_diff_threshold:
+                all_predictions[example["id"]] = ""
+            else:
+                all_predictions[example["id"]] = best_non_null_pred["text"]
+
+        # Make `predictions` JSON-serializable by casting np.float back to float.
+        all_nbest_json[example["id"]] = [
+            {k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
+            for pred in predictions
+        ]
+
+    # If we have an output_dir, let's save all those dicts.
+    if output_dir is not None:
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
+
+        prediction_file = os.path.join(
+            output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
+        )
+        nbest_file = os.path.join(
+            output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
+        )
+        if version_2_with_negative:
+            null_odds_file = os.path.join(
+                output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
+            )
+
+        logger.info(f"Saving predictions to {prediction_file}.")
+        with open(prediction_file, "w") as writer:
+            writer.write(json.dumps(all_predictions, indent=4) + "\n")
+        logger.info(f"Saving nbest_preds to {nbest_file}.")
+        with open(nbest_file, "w") as writer:
+            writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+        if version_2_with_negative:
+            logger.info(f"Saving null_odds to {null_odds_file}.")
+            with open(null_odds_file, "w") as writer:
+                writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+    return all_predictions
+
+
+def postprocess_qa_predictions_with_beam_search(
+    examples,
+    features,
+    predictions: Tuple[np.ndarray, np.ndarray],
+    version_2_with_negative: bool = False,
+    n_best_size: int = 20,
+    max_answer_length: int = 30,
+    start_n_top: int = 5,
+    end_n_top: int = 5,
+    output_dir: Optional[str] = None,
+    prefix: Optional[str] = None,
+    log_level: Optional[int] = logging.WARNING,
+):
+    """
+    Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the
+    original contexts. This is the postprocessing functions for models that return start and end logits, indices, as well as
+    cls token predictions.
+
+    Args:
+        examples: The non-preprocessed dataset (see the main script for more information).
+        features: The processed dataset (see the main script for more information).
+        predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
+            The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
+            first dimension must match the number of elements of :obj:`features`.
+        version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not the underlying dataset contains examples with no answers.
+        n_best_size (:obj:`int`, `optional`, defaults to 20):
+            The total number of n-best predictions to generate when looking for an answer.
+        max_answer_length (:obj:`int`, `optional`, defaults to 30):
+            The maximum length of an answer that can be generated. This is needed because the start and end predictions
+            are not conditioned on one another.
+        start_n_top (:obj:`int`, `optional`, defaults to 5):
+            The number of top start logits too keep when searching for the :obj:`n_best_size` predictions.
+        end_n_top (:obj:`int`, `optional`, defaults to 5):
+            The number of top end logits too keep when searching for the :obj:`n_best_size` predictions.
+        output_dir (:obj:`str`, `optional`):
+            If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
+            :obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
+            answers, are saved in `output_dir`.
+        prefix (:obj:`str`, `optional`):
+            If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
+        log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
+            ``logging`` log level (e.g., ``logging.WARNING``)
+    """
+    if len(predictions) != 5:
+        raise ValueError("`predictions` should be a tuple with five elements.")
+    start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = predictions
+
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
+
+    # Build a map example to its corresponding features.
+    example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
+    features_per_example = collections.defaultdict(list)
+    for i, feature in enumerate(features):
+        features_per_example[example_id_to_index[feature["example_id"]]].append(i)
+
+    # The dictionaries we have to fill.
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    scores_diff_json = collections.OrderedDict() if version_2_with_negative else None
+
+    # Logging.
+    logger.setLevel(log_level)
+    logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
+
+    # Let's loop over all the examples!
+    for example_index, example in enumerate(tqdm(examples)):
+        # Those are the indices of the features associated to the current example.
+        feature_indices = features_per_example[example_index]
+
+        min_null_score = None
+        prelim_predictions = []
+
+        # Looping through all the features associated to the current example.
+        for feature_index in feature_indices:
+            # We grab the predictions of the model for this feature.
+            start_log_prob = start_top_log_probs[feature_index]
+            start_indexes = start_top_index[feature_index]
+            end_log_prob = end_top_log_probs[feature_index]
+            end_indexes = end_top_index[feature_index]
+            feature_null_score = cls_logits[feature_index]
+            # This is what will allow us to map some the positions in our logits to span of texts in the original
+            # context.
+            offset_mapping = features[feature_index]["offset_mapping"]
+            # Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
+            # available in the current feature.
+            token_is_max_context = features[feature_index].get("token_is_max_context", None)
+
+            # Update minimum null prediction
+            if min_null_score is None or feature_null_score < min_null_score:
+                min_null_score = feature_null_score
+
+            # Go through all possibilities for the `n_start_top`/`n_end_top` greater start and end logits.
+            for i in range(start_n_top):
+                for j in range(end_n_top):
+                    start_index = int(start_indexes[i])
+                    j_index = i * end_n_top + j
+                    end_index = int(end_indexes[j_index])
+                    # Don't consider out-of-scope answers (last part of the test should be unnecessary because of the
+                    # p_mask but let's not take any risk)
+                    if (
+                        start_index >= len(offset_mapping)
+                        or end_index >= len(offset_mapping)
+                        or offset_mapping[start_index] is None
+                        or len(offset_mapping[start_index]) < 2
+                        or offset_mapping[end_index] is None
+                        or len(offset_mapping[end_index]) < 2
+                    ):
+                        continue
+
+                    # Don't consider answers with a length negative or > max_answer_length.
+                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:
+                        continue
+                    # Don't consider answer that don't have the maximum context available (if such information is
+                    # provided).
+                    if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
+                        continue
+                    prelim_predictions.append(
+                        {
+                            "offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
+                            "score": start_log_prob[i] + end_log_prob[j_index],
+                            "start_log_prob": start_log_prob[i],
+                            "end_log_prob": end_log_prob[j_index],
+                        }
+                    )
+
+        # Only keep the best `n_best_size` predictions.
+        predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
+
+        # Use the offsets to gather the answer text in the original context.
+        context = example["context"]
+        for pred in predictions:
+            offsets = pred.pop("offsets")
+            pred["text"] = context[offsets[0] : offsets[1]]
+
+        # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
+        # failure.
+        if len(predictions) == 0:
+            # Without predictions min_null_score is going to be None and None will cause an exception later
+            min_null_score = -2e-6
+            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": min_null_score})
+
+        # Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
+        # the LogSumExp trick).
+        scores = np.array([pred.pop("score") for pred in predictions])
+        exp_scores = np.exp(scores - np.max(scores))
+        probs = exp_scores / exp_scores.sum()
+
+        # Include the probabilities in our predictions.
+        for prob, pred in zip(probs, predictions):
+            pred["probability"] = prob
+
+        # Pick the best prediction and set the probability for the null answer.
+        all_predictions[example["id"]] = predictions[0]["text"]
+        if version_2_with_negative:
+            scores_diff_json[example["id"]] = float(min_null_score)
+
+        # Make `predictions` JSON-serializable by casting np.float back to float.
+        all_nbest_json[example["id"]] = [
+            {k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
+            for pred in predictions
+        ]
+
+    # If we have an output_dir, let's save all those dicts.
+    if output_dir is not None:
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
+
+        prediction_file = os.path.join(
+            output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
+        )
+        nbest_file = os.path.join(
+            output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
+        )
+        if version_2_with_negative:
+            null_odds_file = os.path.join(
+                output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
+            )
+
+        logger.info(f"Saving predictions to {prediction_file}.")
+        with open(prediction_file, "w") as writer:
+            writer.write(json.dumps(all_predictions, indent=4) + "\n")
+        logger.info(f"Saving nbest_preds to {nbest_file}.")
+        with open(nbest_file, "w") as writer:
+            writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+        if version_2_with_negative:
+            logger.info(f"Saving null_odds to {null_odds_file}.")
+            with open(null_odds_file, "w") as writer:
+                writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+    return all_predictions, scores_diff_json
diff --git a/hpo-examples/requirements.txt b/hpo-examples/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5d27d62b25040d17c8244f442a62448bd5cb2603
--- /dev/null
+++ b/hpo-examples/requirements.txt
@@ -0,0 +1,127 @@
+absl-py @ file:///home/conda/feedstock_root/build_artifacts/absl-py_1733730548347/work
+accelerate==1.4.0
+aiohappyeyeballs==2.4.6
+aiohttp==3.11.12
+aiosignal==1.3.2
+albucore==0.0.23
+albumentations==2.0.4
+annotated-types==0.7.0
+async-timeout==5.0.1
+attrs==25.1.0
+audioread==3.0.1
+Brotli==1.1.0
+certifi==2025.1.31
+cffi==1.17.1
+charset-normalizer==3.4.1
+click==8.1.8
+colorama==0.4.6
+contourpy==1.3.0
+cycler==0.12.1
+datasets==3.3.1
+decorator==5.1.1
+dill==0.3.8
+eval_type_backport==0.2.2
+evaluate==0.4.3
+filelock==3.13.1
+fonttools==4.56.0
+frozenlist==1.5.0
+fsspec==2024.6.1
+grpcio @ file:///croot/grpc-split_1716834572340/work
+huggingface-hub==0.29.1
+idna==3.10
+importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1737420181517/work
+importlib_resources==6.5.2
+inflate64==1.0.1
+Jinja2==3.1.4
+jiwer==3.1.0
+joblib==1.4.2
+kiwisolver==1.4.7
+lazy_loader==0.4
+librosa==0.10.2.post1
+llvmlite==0.43.0
+lxml==5.3.2
+Markdown @ file:///home/conda/feedstock_root/build_artifacts/markdown_1710435156458/work
+MarkupSafe==2.1.5
+matplotlib==3.9.4
+mpmath==1.3.0
+msgpack==1.1.0
+multidict==6.1.0
+multiprocess==0.70.16
+multivolumefile==0.2.3
+networkx==3.2.1
+numba==0.60.0
+numpy==1.26.3
+numpy-stl==3.2.0
+nvidia-cublas-cu11==11.11.3.6
+nvidia-cuda-cupti-cu11==11.8.87
+nvidia-cuda-nvrtc-cu11==11.8.89
+nvidia-cuda-runtime-cu11==11.8.89
+nvidia-cudnn-cu11==9.1.0.70
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.3.0.86
+nvidia-cusolver-cu11==11.4.1.48
+nvidia-cusparse-cu11==11.7.5.86
+nvidia-nccl-cu11==2.21.5
+nvidia-nvtx-cu11==11.8.86
+opencv-python-headless==4.11.0.86
+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1733203243479/work
+pandas==2.2.3
+peft==0.15.1
+pillow==11.0.0
+platformdirs==4.3.6
+polars==1.22.0
+pooch==1.8.2
+portalocker==3.1.1
+propcache==0.2.1
+protobuf==4.25.3
+psutil==7.0.0
+py7zr==0.22.0
+pyarrow==19.0.1
+pybcj==1.0.3
+pycocotools==2.0.8
+pycparser==2.22
+pycryptodomex==3.22.0
+pydantic==2.10.6
+pydantic_core==2.27.2
+pyglet==1.5.31
+pyparsing==3.2.1
+pyppmd==1.1.1
+python-dateutil==2.9.0.post0
+python-utils==3.9.1
+pytz==2025.1
+PyYAML==6.0.2
+pyzstd==0.16.2
+RapidFuzz==3.13.0
+regex==2024.11.6
+requests==2.32.3
+sacrebleu==2.5.1
+safetensors==0.5.2
+scikit-learn==1.6.1
+scipy==1.13.1
+sentencepiece==0.2.0
+simsimd==6.2.1
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1733380938961/work
+soundfile==0.13.1
+soxr==0.5.0.post1
+stringzilla==3.12.0
+sympy==1.13.1
+tabulate==0.9.0
+tensorboard @ file:///home/conda/feedstock_root/build_artifacts/tensorboard_1733820672324/work/tensorboard-2.18.0-py3-none-any.whl#sha256=107ca4821745f73e2aefa02c50ff70a9b694f39f790b11e6f682f7d326745eab
+tensorboard_data_server @ file:///croot/tensorboard-data-server_1724172556187/work/tensorboard_data_server-0.7.0-py3-none-manylinux2014_x86_64.whl#sha256=79c85cfe2c0cdd115b61609d332d372681e5dac49d438683075d957adc2048f1
+texttable==1.7.0
+threadpoolctl==3.5.0
+tokenizers==0.21.0
+torch==2.6.0+cu118
+torchaudio==2.6.0+cu118
+torchvision==0.21.0+cu118
+tqdm==4.67.1
+transformers==4.49.0
+trimesh==4.6.3
+triton==3.2.0
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0
+Werkzeug @ file:///home/conda/feedstock_root/build_artifacts/werkzeug_1644332431572/work
+xxhash==3.5.0
+yarl==1.18.3
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1732827521216/work
diff --git a/hpo-examples/semantic-segmentation/coco_utils.py b/hpo-examples/semantic-segmentation/coco_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e02434012f1fc3517a284be04aa058a3ac32b79d
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/coco_utils.py
@@ -0,0 +1,108 @@
+import copy
+import os
+
+import torch
+import torch.utils.data
+import torchvision
+from PIL import Image
+from pycocotools import mask as coco_mask
+from transforms import Compose
+
+
+class FilterAndRemapCocoCategories:
+    def __init__(self, categories, remap=True):
+        self.categories = categories
+        self.remap = remap
+
+    def __call__(self, image, anno):
+        anno = [obj for obj in anno if obj["category_id"] in self.categories]
+        if not self.remap:
+            return image, anno
+        anno = copy.deepcopy(anno)
+        for obj in anno:
+            obj["category_id"] = self.categories.index(obj["category_id"])
+        return image, anno
+
+
+def convert_coco_poly_to_mask(segmentations, height, width):
+    masks = []
+    for polygons in segmentations:
+        rles = coco_mask.frPyObjects(polygons, height, width)
+        mask = coco_mask.decode(rles)
+        if len(mask.shape) < 3:
+            mask = mask[..., None]
+        mask = torch.as_tensor(mask, dtype=torch.uint8)
+        mask = mask.any(dim=2)
+        masks.append(mask)
+    if masks:
+        masks = torch.stack(masks, dim=0)
+    else:
+        masks = torch.zeros((0, height, width), dtype=torch.uint8)
+    return masks
+
+
+class ConvertCocoPolysToMask:
+    def __call__(self, image, anno):
+        w, h = image.size
+        segmentations = [obj["segmentation"] for obj in anno]
+        cats = [obj["category_id"] for obj in anno]
+        if segmentations:
+            masks = convert_coco_poly_to_mask(segmentations, h, w)
+            cats = torch.as_tensor(cats, dtype=masks.dtype)
+            # merge all instance masks into a single segmentation map
+            # with its corresponding categories
+            target, _ = (masks * cats[:, None, None]).max(dim=0)
+            # discard overlapping instances
+            target[masks.sum(0) > 1] = 255
+        else:
+            target = torch.zeros((h, w), dtype=torch.uint8)
+        target = Image.fromarray(target.numpy())
+        return image, target
+
+
+def _coco_remove_images_without_annotations(dataset, cat_list=None):
+    def _has_valid_annotation(anno):
+        # if it's empty, there is no annotation
+        if len(anno) == 0:
+            return False
+        # if more than 1k pixels occupied in the image
+        return sum(obj["area"] for obj in anno) > 1000
+
+    if not isinstance(dataset, torchvision.datasets.CocoDetection):
+        raise TypeError(
+            f"This function expects dataset of type torchvision.datasets.CocoDetection, instead  got {type(dataset)}"
+        )
+
+    ids = []
+    for ds_idx, img_id in enumerate(dataset.ids):
+        ann_ids = dataset.coco.getAnnIds(imgIds=img_id, iscrowd=None)
+        anno = dataset.coco.loadAnns(ann_ids)
+        if cat_list:
+            anno = [obj for obj in anno if obj["category_id"] in cat_list]
+        if _has_valid_annotation(anno):
+            ids.append(ds_idx)
+
+    dataset = torch.utils.data.Subset(dataset, ids)
+    return dataset
+
+
+def get_coco(root, image_set, transforms):
+    PATHS = {
+        "train": ("train2017", os.path.join("annotations", "instances_train2017.json")),
+        "val": ("val2017", os.path.join("annotations", "instances_val2017.json")),
+        # "train": ("val2017", os.path.join("annotations", "instances_val2017.json"))
+    }
+    CAT_LIST = [0, 5, 2, 16, 9, 44, 6, 3, 17, 62, 21, 67, 18, 19, 4, 1, 64, 20, 63, 7, 72]
+
+    transforms = Compose([FilterAndRemapCocoCategories(CAT_LIST, remap=True), ConvertCocoPolysToMask(), transforms])
+
+    img_folder, ann_file = PATHS[image_set]
+    img_folder = os.path.join(root, img_folder)
+    ann_file = os.path.join(root, ann_file)
+
+    dataset = torchvision.datasets.CocoDetection(img_folder, ann_file, transforms=transforms)
+
+    if image_set == "train":
+        dataset = _coco_remove_images_without_annotations(dataset, CAT_LIST)
+
+    return dataset
diff --git a/hpo-examples/semantic-segmentation/deeplabv3_resnet101/model_4.pth b/hpo-examples/semantic-segmentation/deeplabv3_resnet101/model_4.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f8e1c13624ef1e152524e02d20945d9762489565
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/deeplabv3_resnet101/model_4.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27d8158b45e7d91fb6518fc6ac2e3e5e56e90d3361d7ebae93ddfdfe34ec6ef9
+size 551683014
diff --git a/hpo-examples/semantic-segmentation/deeplabv3_resnet50/model_4.pth b/hpo-examples/semantic-segmentation/deeplabv3_resnet50/model_4.pth
new file mode 100644
index 0000000000000000000000000000000000000000..4f1aa1f3f6c1602d702dfca0d6afe37fb76c3d2f
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/deeplabv3_resnet50/model_4.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 399392768
diff --git a/hpo-examples/semantic-segmentation/fcn_resnet101/model_4.pth b/hpo-examples/semantic-segmentation/fcn_resnet101/model_4.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f3f9ceea9634c1ca0bcc0e01d1ade372aecc4960
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/fcn_resnet101/model_4.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0cff89a3247cab4c9ffef3b65eb602d1e86e673fbe76d6b1486baaa3b693da39
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diff --git a/hpo-examples/semantic-segmentation/fcn_resnet50/model_4.pth b/hpo-examples/semantic-segmentation/fcn_resnet50/model_4.pth
new file mode 100644
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--- /dev/null
+++ b/hpo-examples/semantic-segmentation/fcn_resnet50/model_4.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 345910764
diff --git a/hpo-examples/semantic-segmentation/presets.py b/hpo-examples/semantic-segmentation/presets.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed02ae660e4e4272b37cf1970ed3e1d8dcfeba2c
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/presets.py
@@ -0,0 +1,39 @@
+import torch
+import transforms as T
+
+
+class SegmentationPresetTrain:
+    def __init__(self, *, base_size, crop_size, hflip_prob=0.5, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
+        min_size = int(0.5 * base_size)
+        max_size = int(2.0 * base_size)
+
+        trans = [T.RandomResize(min_size, max_size)]
+        if hflip_prob > 0:
+            trans.append(T.RandomHorizontalFlip(hflip_prob))
+        trans.extend(
+            [
+                T.RandomCrop(crop_size),
+                T.PILToTensor(),
+                T.ConvertImageDtype(torch.float),
+                T.Normalize(mean=mean, std=std),
+            ]
+        )
+        self.transforms = T.Compose(trans)
+
+    def __call__(self, img, target):
+        return self.transforms(img, target)
+
+
+class SegmentationPresetEval:
+    def __init__(self, *, base_size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
+        self.transforms = T.Compose(
+            [
+                T.RandomResize(base_size, base_size),
+                T.PILToTensor(),
+                T.ConvertImageDtype(torch.float),
+                T.Normalize(mean=mean, std=std),
+            ]
+        )
+
+    def __call__(self, img, target):
+        return self.transforms(img, target)
diff --git a/hpo-examples/semantic-segmentation/run.sh b/hpo-examples/semantic-segmentation/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..df3434b0fc05a7c3b0bf7490e507824252b5148c
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/run.sh
@@ -0,0 +1,104 @@
+# ✅ global correct: 90.9
+# average row correct: ['93.7', '81.9', '80.3', '75.3', '38.0', '57.3', '80.8', '69.2', '88.2', '41.9', '76.2', '63.8', '70.7', '84.8', '88.5', '90.8', '45.1', '68.3', '51.4', '79.6', '74.3']
+# IoU: ['89.7', '67.8', '66.5', '60.2', '34.6', '45.9', '75.4', '55.0', '77.0', '33.8', '64.5', '33.3', '48.0', '71.3', '79.1', '80.7', '27.7', '55.3', '40.1', '72.8', '57.9']
+# mean IoU: 58.9
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model fcn_resnet50 --aux-loss --weights FCN_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1\
+    --test-only
+
+# ✅ global correct: 91.1
+# average row correct: ['93.9', '82.1', '79.2', '77.1', '37.9', '57.8', '81.9', '69.1', '91.3', '40.5', '80.0', '63.4', '66.9', '84.5', '90.7', '90.8', '44.4', '69.3', '50.5', '84.2', '74.0']
+# IoU: ['89.9', '70.0', '67.0', '61.2', '34.4', '47.0', '74.8', '56.1', '77.5', '33.7', '64.3', '33.5', '49.9', '71.2', '79.6', '81.2', '27.7', '55.3', '40.8', '74.6', '60.5']
+# mean IoU: 59.5
+# Training time 2:40:17
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model fcn_resnet50 --aux-loss --output-dir fcn_resnet50 --weights FCN_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1\
+    --epochs 5 --batch-size 16 --lr 0.0002 --aux-loss --print-freq 100\
+    --lr-warmup-method constant --lr-warmup-epochs 3 --lr-warmup-decay 0. \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model fcn_resnet50 --aux-loss --resume fcn_resnet50/model_4.pth\
+    --test-only
+
+
+# ✅ global correct: 91.1
+# average row correct: ['93.6', '83.1', '81.1', '77.1', '43.8', '65.3', '80.0', '69.8', '92.0', '47.9', '82.8', '66.5', '71.5', '85.6', '90.6', '91.2', '44.0', '70.1', '59.3', '86.5', '77.5']
+# IoU: ['89.8', '68.1', '68.9', '61.9', '36.8', '52.5', '76.5', '60.8', '80.8', '38.7', '71.9', '33.2', '52.4', '72.4', '81.4', '82.4', '27.6', '60.1', '42.7', '80.9', '65.7']
+# mean IoU: 62.2
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model fcn_resnet101 --aux-loss --weights FCN_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1\
+    --test-only
+
+# ✅ global correct: 91.1
+# average row correct: ['93.6', '82.8', '79.6', '78.1', '45.2', '64.2', '82.8', '69.2', '92.9', '46.5', '85.3', '65.8', '69.7', '84.9', '92.5', '91.3', '45.7', '72.0', '55.6', '88.9', '77.9']
+# IoU: ['89.8', '66.9', '69.3', '61.8', '37.3', '52.1', '77.8', '61.0', '80.5', '37.8', '70.4', '32.8', '53.5', '73.4', '81.1', '82.2', '28.8', '60.7', '44.5', '81.9', '66.0']
+# mean IoU: 62.4
+# Training time 3:23:30
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model fcn_resnet101 --aux-loss --output-dir fcn_resnet101 --weights FCN_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1\
+    --epochs 5 --batch-size 12 --lr 0.0002 --aux-loss --print-freq 100\
+    --lr-warmup-method constant --lr-warmup-epochs 3 --lr-warmup-decay 0. \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model fcn_resnet101 --aux-loss --resume fcn_resnet101/model_4.pth\
+    --test-only
+
+
+# ✅ global correct: 91.5
+# average row correct: ['93.8', '86.1', '83.2', '81.6', '46.6', '57.3', '88.7', '74.4', '93.9', '50.0', '86.9', '61.4', '81.8', '87.4', '93.5', '92.1', '48.7', '89.4', '63.6', '89.1', '73.5']
+# IoU: ['90.2', '68.6', '74.6', '60.2', '41.0', '43.6', '85.3', '54.3', '86.1', '39.3', '81.2', '33.0', '59.0', '71.4', '82.6', '82.7', '25.7', '74.0', '48.6', '75.6', '62.9']
+# mean IoU: 63.8
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model deeplabv3_resnet50 --aux-loss --weights DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1\
+    --test-only
+
+
+# ✅ global correct: 91.6
+# average row correct: ['94.1', '85.8', '82.4', '81.5', '44.5', '56.6', '87.3', '73.0', '94.2', '48.2', '87.6', '60.4', '80.1', '87.9', '93.3', '92.0', '47.6', '88.2', '62.9', '88.5', '73.9']
+# IoU: ['90.4', '70.0', '74.6', '62.1', '40.5', '45.2', '83.9', '55.5', '85.7', '39.1', '81.4', '32.6', '60.9', '71.9', '82.6', '82.9', '27.4', '73.5', '47.4', '79.9', '63.7']
+# mean IoU: 64.3
+# # Training time 3:38:30
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model deeplabv3_resnet50 --aux-loss --output-dir deeplabv3_resnet50 --weights DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1\
+    --epochs 5 --batch-size 16 --lr 0.0002 --aux-loss --print-freq 100\
+    --lr-warmup-method constant --lr-warmup-epochs 3 --lr-warmup-decay 0. \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model deeplabv3_resnet50 --aux-loss --resume deeplabv3_resnet50/model_4.pth\
+    --test-only
+
+
+# ✅ global correct: 91.7
+# average row correct: ['94.0', '83.0', '82.9', '82.2', '44.7', '61.0', '86.9', '70.3', '93.6', '54.4', '84.7', '60.5', '82.2', '90.0', '92.4', '92.3', '49.1', '91.8', '66.0', '90.0', '79.7']
+# IoU: ['90.4', '71.3', '74.8', '58.2', '38.9', '49.7', '82.9', '61.9', '86.1', '42.9', '79.1', '32.8', '67.7', '75.1', '81.6', '83.3', '26.9', '74.8', '49.7', '76.4', '66.9']
+# mean IoU: 65.3
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model deeplabv3_resnet101 --aux-loss --weights DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1\
+    --test-only
+
+
+# ✅ global correct: 91.8
+# average row correct: ['94.2', '81.6', '82.2', '81.1', '43.2', '60.8', '86.4', '70.6', '94.4', '52.2', '85.1', '60.0', '82.7', '90.3', '92.9', '92.3', '47.6', '90.5', '66.1', '89.7', '77.9']
+# IoU: ['90.5', '72.6', '74.7', '60.9', '37.9', '49.6', '82.6', '62.4', '86.9', '41.9', '79.5', '32.6', '68.4', '75.3', '82.1', '83.3', '28.4', '74.3', '50.5', '77.4', '67.7']
+# mean IoU: 65.7
+# Training time 4:21:32
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model deeplabv3_resnet101 --aux-loss --output-dir deeplabv3_resnet101 --weights DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1\
+    --epochs 5 --batch-size 12 --lr 0.0002 --aux-loss --print-freq 100\
+    --lr-warmup-method constant --lr-warmup-epochs 3 --lr-warmup-decay 0. \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+torchrun --nproc_per_node=4 train.py\
+    --workers 4 --dataset coco --data-path /home/cs/Documents/datasets/coco\
+    --model deeplabv3_resnet101 --aux-loss --resume deeplabv3_resnet101/model_4.pth\
+    --test-only
\ No newline at end of file
diff --git a/hpo-examples/semantic-segmentation/train.py b/hpo-examples/semantic-segmentation/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b4ae78e6c18357531880889f7f2b1c0a184641a
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/train.py
@@ -0,0 +1,327 @@
+import datetime
+import os
+import time
+import warnings
+
+import presets
+import torch
+import torch.utils.data
+import torchvision
+import utils
+from coco_utils import get_coco
+from torch import nn
+from torch.optim.lr_scheduler import PolynomialLR
+from torchvision.transforms import functional as F, InterpolationMode
+
+from trplib import apply_trp
+
+
+def get_dataset(dir_path, name, image_set, transform):
+    def sbd(*args, **kwargs):
+        return torchvision.datasets.SBDataset(*args, mode="segmentation", **kwargs)
+
+    paths = {
+        "voc": (dir_path, torchvision.datasets.VOCSegmentation, 21),
+        "voc_aug": (dir_path, sbd, 21),
+        "coco": (dir_path, get_coco, 21),
+    }
+    p, ds_fn, num_classes = paths[name]
+
+    ds = ds_fn(p, image_set=image_set, transforms=transform)
+    return ds, num_classes
+
+
+def get_transform(train, args):
+    if train:
+        return presets.SegmentationPresetTrain(base_size=520, crop_size=480)
+    elif args.weights and args.test_only:
+        weights = torchvision.models.get_weight(args.weights)
+        trans = weights.transforms()
+
+        def preprocessing(img, target):
+            img = trans(img)
+            size = F.get_dimensions(img)[1:]
+            target = F.resize(target, size, interpolation=InterpolationMode.NEAREST)
+            return img, F.pil_to_tensor(target)
+
+        return preprocessing
+    else:
+        return presets.SegmentationPresetEval(base_size=520)
+
+
+def criterion(inputs, target):
+    losses = {}
+    for name, x in inputs.items():
+        losses[name] = nn.functional.cross_entropy(x, target, ignore_index=255)
+
+    if len(losses) == 1:
+        return losses["out"]
+
+    return losses["out"] + 0.5 * losses["aux"]
+
+
+def evaluate(model, data_loader, device, num_classes):
+    model.eval()
+    confmat = utils.ConfusionMatrix(num_classes)
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = "Test:"
+    num_processed_samples = 0
+    with torch.inference_mode():
+        for image, target in metric_logger.log_every(data_loader, 100, header):
+            image, target = image.to(device), target.to(device)
+            output = model(image)
+            output = output["out"]
+
+            confmat.update(target.flatten(), output.argmax(1).flatten())
+            # FIXME need to take into account that the datasets
+            # could have been padded in distributed setup
+            num_processed_samples += image.shape[0]
+
+        confmat.reduce_from_all_processes()
+
+    num_processed_samples = utils.reduce_across_processes(num_processed_samples)
+    if (
+        hasattr(data_loader.dataset, "__len__")
+        and len(data_loader.dataset) != num_processed_samples
+        and torch.distributed.get_rank() == 0
+    ):
+        # See FIXME above
+        warnings.warn(
+            f"It looks like the dataset has {len(data_loader.dataset)} samples, but {num_processed_samples} "
+            "samples were used for the validation, which might bias the results. "
+            "Try adjusting the batch size and / or the world size. "
+            "Setting the world size to 1 is always a safe bet."
+        )
+
+    return confmat
+
+
+def train_one_epoch(model, optimizer, data_loader, lr_scheduler, device, epoch, print_freq, scaler=None):
+    model.train()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value}"))
+    header = f"Epoch: [{epoch}]"
+    for image, target in metric_logger.log_every(data_loader, print_freq, header):
+        image, target = image.to(device), target.to(device)
+        with torch.amp.autocast(device_type="cuda", enabled=scaler is not None):
+            _, loss = model(image, target)
+            # output = model(image)
+            # loss = criterion(output, target)
+
+        optimizer.zero_grad()
+        if scaler is not None:
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+        else:
+            loss.backward()
+            optimizer.step()
+
+        lr_scheduler.step()
+
+        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
+
+
+def main(args):
+    if args.output_dir:
+        utils.mkdir(args.output_dir)
+
+    utils.init_distributed_mode(args)
+    print(args)
+
+    device = torch.device(args.device)
+
+    if args.use_deterministic_algorithms:
+        torch.backends.cudnn.benchmark = False
+        torch.use_deterministic_algorithms(True)
+    else:
+        torch.backends.cudnn.benchmark = True
+
+    dataset, num_classes = get_dataset(args.data_path, args.dataset, "train", get_transform(True, args))
+    dataset_test, _ = get_dataset(args.data_path, args.dataset, "val", get_transform(False, args))
+
+    if args.distributed:
+        train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test, shuffle=False)
+    else:
+        train_sampler = torch.utils.data.RandomSampler(dataset)
+        test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        sampler=train_sampler,
+        num_workers=args.workers,
+        collate_fn=utils.collate_fn,
+        drop_last=True,
+    )
+
+    data_loader_test = torch.utils.data.DataLoader(
+        dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers, collate_fn=utils.collate_fn
+    )
+
+    model = torchvision.models.get_model(
+        args.model,
+        weights=args.weights,
+        weights_backbone=args.weights_backbone,
+        num_classes=num_classes,
+        aux_loss=args.aux_loss,
+    )
+    if args.apply_trp:
+        model = apply_trp(model, args.trp_depths, args.trp_p, args.trp_lambdas)
+    model.to(device)
+    if args.distributed:
+        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module
+
+    params_to_optimize = [
+        {"params": [p for p in model_without_ddp.backbone.parameters() if p.requires_grad]},
+        {"params": [p for p in model_without_ddp.classifier.parameters() if p.requires_grad]},
+    ]
+    if args.aux_loss:
+        params = [p for p in model_without_ddp.aux_classifier.parameters() if p.requires_grad]
+        params_to_optimize.append({"params": params, "lr": args.lr * 10})
+    optimizer = torch.optim.SGD(params_to_optimize, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
+
+    scaler = torch.amp.GradScaler(device="cuda") if args.amp else None
+
+    iters_per_epoch = len(data_loader)
+    main_lr_scheduler = PolynomialLR(
+        optimizer, total_iters=iters_per_epoch * (args.epochs - args.lr_warmup_epochs), power=0.9
+    )
+
+    if args.lr_warmup_epochs > 0:
+        warmup_iters = iters_per_epoch * args.lr_warmup_epochs
+        args.lr_warmup_method = args.lr_warmup_method.lower()
+        if args.lr_warmup_method == "linear":
+            warmup_lr_scheduler = torch.optim.lr_scheduler.LinearLR(
+                optimizer, start_factor=args.lr_warmup_decay, total_iters=warmup_iters
+            )
+        elif args.lr_warmup_method == "constant":
+            warmup_lr_scheduler = torch.optim.lr_scheduler.ConstantLR(
+                optimizer, factor=args.lr_warmup_decay, total_iters=warmup_iters
+            )
+        else:
+            raise RuntimeError(
+                f"Invalid warmup lr method '{args.lr_warmup_method}'. Only linear and constant are supported."
+            )
+        lr_scheduler = torch.optim.lr_scheduler.SequentialLR(
+            optimizer, schedulers=[warmup_lr_scheduler, main_lr_scheduler], milestones=[warmup_iters]
+        )
+    else:
+        lr_scheduler = main_lr_scheduler
+
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=False)
+        model_without_ddp.load_state_dict(checkpoint["model"], strict=not args.test_only)
+        if not args.test_only:
+            optimizer.load_state_dict(checkpoint["optimizer"])
+            lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
+            args.start_epoch = checkpoint["epoch"] + 1
+            if args.amp:
+                scaler.load_state_dict(checkpoint["scaler"])
+
+    if args.test_only:
+        # We disable the cudnn benchmarking because it can noticeably affect the accuracy
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+        confmat = evaluate(model, data_loader_test, device=device, num_classes=num_classes)
+        print(confmat)
+        return
+
+    start_time = time.time()
+    for epoch in range(args.start_epoch, args.epochs):
+        if args.distributed:
+            train_sampler.set_epoch(epoch)
+        train_one_epoch(model, optimizer, data_loader, lr_scheduler, device, epoch, args.print_freq, scaler)
+        confmat = evaluate(model, data_loader_test, device=device, num_classes=num_classes)
+        print(confmat)
+
+        if args.output_dir:
+            checkpoint = {
+                "model": model_without_ddp.state_dict() if not args.apply_trp else {k: v for k, v in model_without_ddp.state_dict().items() if (not k.startswith("out_trp_blocks")) or not k.startswith("aux_trp_blocks")},  # NOTE: remove TRP heads
+                "optimizer": optimizer.state_dict(),
+                "lr_scheduler": lr_scheduler.state_dict(),
+                "epoch": epoch,
+                "args": args,
+            }
+            if args.amp:
+                checkpoint["scaler"] = scaler.state_dict()
+        utils.save_on_master(checkpoint, os.path.join(args.output_dir, f"model_{epoch}.pth"))
+        utils.save_on_master(checkpoint, os.path.join(args.output_dir, "checkpoint.pth"))
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print(f"Training time {total_time_str}")
+
+
+def get_args_parser(add_help=True):
+    import argparse
+
+    parser = argparse.ArgumentParser(description="PyTorch Segmentation Training", add_help=add_help)
+
+    parser.add_argument("--data-path", default="/datasets01/COCO/022719/", type=str, help="dataset path")
+    parser.add_argument("--dataset", default="coco", type=str, help="dataset name")
+    parser.add_argument("--model", default="fcn_resnet101", type=str, help="model name")
+    parser.add_argument("--aux-loss", action="store_true", help="auxiliar loss")
+    parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
+    parser.add_argument(
+        "-b", "--batch-size", default=8, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
+    )
+    parser.add_argument("--epochs", default=30, type=int, metavar="N", help="number of total epochs to run")
+
+    parser.add_argument(
+        "-j", "--workers", default=16, type=int, metavar="N", help="number of data loading workers (default: 16)"
+    )
+    parser.add_argument("--lr", default=0.01, type=float, help="initial learning rate")
+    parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")
+    parser.add_argument(
+        "--wd",
+        "--weight-decay",
+        default=1e-4,
+        type=float,
+        metavar="W",
+        help="weight decay (default: 1e-4)",
+        dest="weight_decay",
+    )
+    parser.add_argument("--lr-warmup-epochs", default=0, type=int, help="the number of epochs to warmup (default: 0)")
+    parser.add_argument("--lr-warmup-method", default="linear", type=str, help="the warmup method (default: linear)")
+    parser.add_argument("--lr-warmup-decay", default=0.01, type=float, help="the decay for lr")
+    parser.add_argument("--print-freq", default=10, type=int, help="print frequency")
+    parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
+    parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
+    parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")
+    parser.add_argument(
+        "--test-only",
+        dest="test_only",
+        help="Only test the model",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--use-deterministic-algorithms", action="store_true", help="Forces the use of deterministic algorithms only."
+    )
+    # distributed training parameters
+    parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
+    parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
+
+    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
+    parser.add_argument("--weights-backbone", default=None, type=str, help="the backbone weights enum name to load")
+
+    # Mixed precision training parameters
+    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
+
+    parser.add_argument("--apply-trp", dest="apply_trp", help="Apply TRP to the model", action="store_true")
+    parser.add_argument("--trp-depths", type=int, help="trp depth")
+    parser.add_argument("--trp-p", type=float, help="trp p")
+    parser.add_argument("--trp-lambdas", nargs="+", type=float, help="trp lambdas")
+
+    return parser
+
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/hpo-examples/semantic-segmentation/transforms.py b/hpo-examples/semantic-segmentation/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..518048db2faef36297c4a47c700d7235434fcc0b
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/transforms.py
@@ -0,0 +1,100 @@
+import random
+
+import numpy as np
+import torch
+from torchvision import transforms as T
+from torchvision.transforms import functional as F
+
+
+def pad_if_smaller(img, size, fill=0):
+    min_size = min(img.size)
+    if min_size < size:
+        ow, oh = img.size
+        padh = size - oh if oh < size else 0
+        padw = size - ow if ow < size else 0
+        img = F.pad(img, (0, 0, padw, padh), fill=fill)
+    return img
+
+
+class Compose:
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, image, target):
+        for t in self.transforms:
+            image, target = t(image, target)
+        return image, target
+
+
+class RandomResize:
+    def __init__(self, min_size, max_size=None):
+        self.min_size = min_size
+        if max_size is None:
+            max_size = min_size
+        self.max_size = max_size
+
+    def __call__(self, image, target):
+        size = random.randint(self.min_size, self.max_size)
+        image = F.resize(image, size)
+        target = F.resize(target, size, interpolation=T.InterpolationMode.NEAREST)
+        return image, target
+
+
+class RandomHorizontalFlip:
+    def __init__(self, flip_prob):
+        self.flip_prob = flip_prob
+
+    def __call__(self, image, target):
+        if random.random() < self.flip_prob:
+            image = F.hflip(image)
+            target = F.hflip(target)
+        return image, target
+
+
+class RandomCrop:
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, image, target):
+        image = pad_if_smaller(image, self.size)
+        target = pad_if_smaller(target, self.size, fill=255)
+        crop_params = T.RandomCrop.get_params(image, (self.size, self.size))
+        image = F.crop(image, *crop_params)
+        target = F.crop(target, *crop_params)
+        return image, target
+
+
+class CenterCrop:
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, image, target):
+        image = F.center_crop(image, self.size)
+        target = F.center_crop(target, self.size)
+        return image, target
+
+
+class PILToTensor:
+    def __call__(self, image, target):
+        image = F.pil_to_tensor(image)
+        target = torch.as_tensor(np.array(target), dtype=torch.int64)
+        return image, target
+
+
+class ConvertImageDtype:
+    def __init__(self, dtype):
+        self.dtype = dtype
+
+    def __call__(self, image, target):
+        image = F.convert_image_dtype(image, self.dtype)
+        return image, target
+
+
+class Normalize:
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, image, target):
+        image = F.normalize(image, mean=self.mean, std=self.std)
+        return image, target
diff --git a/hpo-examples/semantic-segmentation/trplib.py b/hpo-examples/semantic-segmentation/trplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0fb33879b1ab05f53e0b8e7068729abbb96146
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/trplib.py
@@ -0,0 +1,1181 @@
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from torchvision.models.mobilenetv2 import MobileNetV2
+from torchvision.models.resnet import ResNet
+from torchvision.models.efficientnet import EfficientNet
+from torchvision.models.vision_transformer import VisionTransformer
+from torchvision.models.segmentation.fcn import FCN
+from torchvision.models.segmentation.deeplabv3 import DeepLabV3
+
+import transformers
+from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, CausalLMOutput, Seq2SeqLMOutput
+
+from typing import Optional, Tuple, List, Union, Callable
+from collections import OrderedDict
+import types
+
+
+def trp_criterion(trp_blocks: nn.ModuleList, shared_head: Callable, criterion: Callable, lambdas: List[float], hidden_states: Tensor, logits: Tensor, targets: Tensor, loss_normalization=False):
+    loss, mask = criterion(logits, targets)
+    if loss_normalization:
+        coeff = loss.detach()
+
+    embeds = [hidden_states]
+    predictions = []
+    for k, c in enumerate(lambdas):
+        embeds.append(trp_blocks[k](embeds[-1]))
+        predictions.append(shared_head(embeds[-1]))
+        replica_loss, mask = criterion(predictions[-1], targets, mask)
+        loss += c * replica_loss
+    
+    if loss_normalization:
+        with torch.no_grad():
+            coeff = torch.exp(coeff) / torch.exp(loss.detach())
+        loss = coeff * loss
+    
+    return loss
+
+
+class TPBlock(nn.Module):
+    def __init__(self, depths: int, in_features: int, p: float, dim=-1):
+        super(TPBlock, self).__init__()
+
+        self.dropout = nn.Dropout(p)
+
+        self.cdim = dim
+
+        blocks = []
+        for _ in range(depths):
+            blocks.append(nn.Linear(in_features, in_features))
+            nn.init.constant_(blocks[-1].weight, 0.0)
+            nn.init.constant_(blocks[-1].bias, 0.0)
+            blocks.append(nn.ReLU())
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        x = self.dropout(x)
+        if self.cdim == -1:
+            x = x + self.blocks(x)
+        else:
+            x = x + torch.movedim(self.blocks(torch.movedim(x, self.cdim, -1)), -1, self.cdim)
+        return x
+    
+
+class Config:
+    @staticmethod
+    def gen_criterion(*args, **kwargs):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor.
+                target (Tensor): Target labels.
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor with the same shape of target.
+            """
+            pass
+        return func
+    
+    @staticmethod
+    def gen_shared_head(*args, **kwargs):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States tensor.
+
+            Returns:
+                logits (Tensor): Logits tensor.
+            """
+            pass
+        return func
+
+    @staticmethod
+    def forward(*args, **kwargs):
+        pass
+
+
+# Wav2Vec2 for Audio Classification
+class Wav2Vec2ForSequenceClassificationConfig(Config): 
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self, attention_mask):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+            output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            if self.config.use_weighted_layer_sum:
+                hidden_states = outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION]
+                hidden_states = torch.stack(hidden_states, dim=1)
+                norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+                hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+            else:
+                hidden_states = outputs[0]
+
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                shared_head = Wav2Vec2ForSequenceClassificationConfig.gen_shared_head(self, attention_mask)
+                criterion = Wav2Vec2ForSequenceClassificationConfig.gen_criterion()
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states, logits.view(-1, self.config.num_labels),  labels.view(-1), loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# MobileNetV2 for Image Classification
+class MobileNetV2Config(Config):
+    @staticmethod
+    def gen_criterion(label_smoothing=0.0, top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B] or [B, C].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            label = torch.argmax(target, dim=1) if label_smoothing > 0.0 else target
+                
+            unmasked_loss = F.cross_entropy(input, label, reduction="none", label_smoothing=label_smoothing)
+            if mask is None:
+                mask = torch.ones_like(unmasked_loss, dtype=torch.float32, device=target.device)
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=-1)
+                mask = mask * torch.eq(topk_indices, label[:, None]).any(dim=-1).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = MobileNetV2Config.gen_shared_head(self)
+                criterion = MobileNetV2Config.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ResNet for Image Classification
+class ResNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.fc(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.conv1(images)
+            x = self.bn1(x)
+            x = self.relu(x)
+            x = self.maxpool(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.fc(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = ResNetConfig.gen_shared_head(self)
+                criterion = ResNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# EfficientNet for Image Classification
+class EfficientNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = EfficientNetConfig.gen_shared_head(self)
+                criterion = EfficientNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ViT for Image Classification
+class VisionTransformerConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.heads(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self._process_input(images)
+            n = x.shape[0]
+            batch_class_token = self.class_token.expand(n, -1, -1)
+            x = torch.cat([batch_class_token, x], dim=1)
+            x = self.encoder(x)
+            x = x[:, 0]
+
+            logits = self.heads(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = VisionTransformerConfig.gen_shared_head(self)
+                criterion = VisionTransformerConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# Bert for Question Answering
+class BertForQuestionAnsweringConfig(Config): 
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target: List[Tensor], mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C, 2].
+                target (List[Tensor]): 
+                    Start Positions of shape [B].
+                    End Positions of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            start_positions, end_positions = target
+
+            if mask is None:
+                mask = torch.ones_like(start_positions, dtype=torch.float32, device=start_positions.device)
+
+            start_logits, end_logits = input.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            masked_start_losses = F.cross_entropy(start_logits, start_positions, ignore_index=ignored_index, reduction="none")
+            start_loss = torch.sum(mask * masked_start_losses) / (torch.sum(mask) + 1e-6)
+            masked_end_losses = F.cross_entropy(end_logits, end_positions, ignore_index=ignored_index, reduction="none")
+            end_loss = torch.sum(mask * masked_end_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(start_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, start_positions[:, None]).any(dim=1).to(start_logits.dtype)
+                topk_values, topk_indices = torch.topk(end_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, end_positions[:, None]).any(dim=1).to(end_logits.dtype)
+
+            return (start_loss + end_loss) / 2, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.qa_outputs(hidden_states)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            start_positions: Optional[torch.Tensor] = None,
+            end_positions: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+            r"""
+            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the start of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the end of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            sequence_output = outputs[0]
+
+            logits = self.qa_outputs(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                shared_head = BertForQuestionAnsweringConfig.gen_shared_head(self)
+                criterion = BertForQuestionAnsweringConfig.gen_criterion()
+                total_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, [start_positions, end_positions], loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+    
+
+# FCN for Semantic Segmentation
+class FCNConfig(Config):
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input Tensor: input tensor of shape [B, C, H, W].
+                target (Tensor): Target labels of shape [B, H, W].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B, H, W].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+                
+            masked_loss = F.cross_entropy(input, target, ignore_index=255, reduction="none")
+            loss = torch.sum(mask * masked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, target[:, None, :, :]).any(dim=1).to(input.dtype)
+                # mask = mask * torch.eq(torch.argmax(x, dim=1), target).to(x.dtype)
+                
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_out_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+    
+    @staticmethod
+    def gen_aux_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.aux_classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            input_shape = images.shape[-2:]
+            # contract: features is a dict of tensors
+            features = self.backbone(images)
+
+            result = OrderedDict()
+            x = features["out"]
+            x = self.classifier(x)
+            x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            result["out"] = x
+
+            if self.aux_classifier is not None:
+                x = features["aux"]
+                x = self.aux_classifier(x)
+                x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+                result["aux"] = x
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                out_shared_head = FCNConfig.gen_out_shared_head(self, input_shape)
+                aux_shared_head = FCNConfig.gen_aux_shared_head(self, input_shape)
+                criterion = FCNConfig.gen_criterion(top_k)
+                out_loss = trp_criterion(self.out_trp_blocks, out_shared_head, criterion, lambdas, features["out"], result["out"], targets, loss_normalization)
+                aux_loss = trp_criterion(self.aux_trp_blocks, aux_shared_head, criterion, lambdas, features["aux"], result["aux"], targets, loss_normalization)
+                loss = out_loss + 0.5 * aux_loss
+                return result, loss
+            return result
+        return func
+
+
+# DeepLabV3Config for Semantic Segmentation
+class DeepLabV3Config(FCNConfig):
+    pass
+
+
+# Bert for Text Classification
+class BertForSequenceClassificationConfig(Config): 
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, pooled_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Boberta for Text Classification
+class RobertaForSequenceClassificationConfig(BertForSequenceClassificationConfig): 
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.roberta(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            sequence_output = outputs[0]
+            logits = self.classifier(sequence_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                # move labels to correct device to enable model parallelism
+                labels = labels.to(logits.device)
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Wav2Vec2 for Speech Recognition
+class Wav2Vec2ForCTCConfig(Config):
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def greedy_decode_ctc(
+        log_probs: torch.Tensor,
+        input_lengths: torch.Tensor,
+        blank_token_id: int,
+        target_lengths: torch.Tensor
+    ):
+        """
+        Convert logits to flattened predictions that match the shape of flattened_targets.
+
+        Args:
+            log_probs: [B, L, V] - log-softmax output
+            input_lengths: [B] - actual length of each input
+            blank_token_id: int - index of blank token
+            target_lengths: [B] - used to determine how many predictions to keep per sample
+
+        Returns:
+            flattened_predictions: 1D tensor, same total length as sum(target_lengths)
+        """
+        batch_size = log_probs.size(0)
+        decoded_all = []
+
+        predicted_ids = log_probs.argmax(dim=-1)  # [B, L]
+
+        for i in range(batch_size):
+            pred = predicted_ids[i][:input_lengths[i]]  # [Li]
+            prev = None
+            decoded = []
+            for token in pred:
+                token = token.item()
+                if token != blank_token_id and token != prev:
+                    decoded.append(token)
+                prev = token
+            # Trim or pad to match target_lengths[i]
+            tgt_len = target_lengths[i].item()
+            if len(decoded) >= tgt_len:
+                decoded = decoded[:tgt_len]
+            else:
+                decoded = decoded + [blank_token_id] * (tgt_len - len(decoded))  # pad with blank
+            decoded_all.extend(decoded)
+
+        return torch.tensor(decoded_all, dtype=torch.long, device=log_probs.device)  # shape: [sum(target_lengths)]
+
+    @staticmethod
+    def gen_criterion(input_lengths: Tensor, pad_token_id: int, ctc_zero_infinity: bool):
+        def func(logits: Tensor, labels: Tensor, mask=None):
+            """
+            Args:
+                logits (Tensor): Log Probablities of shape [B, L, V].
+                labels (Tensor): Flattened Targets of shape [B, L'].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(input_lengths, dtype=torch.float32, device=input_lengths.device)
+
+            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            with torch.backends.cudnn.flags(enabled=False):
+                masked_losses = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=pad_token_id, reduction="none", zero_infinity=ctc_zero_infinity)
+                loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                thres = 0.5
+                flattened_predictions = Wav2Vec2ForCTCConfig.greedy_decode_ctc(
+                    log_probs.transpose(0, 1),  # [B, T, V]
+                    input_lengths=input_lengths,
+                    blank_token_id=pad_token_id,
+                    target_lengths=target_lengths
+                )
+                token_wise_mask =  torch.eq(flattened_predictions, flattened_targets).to(flattened_targets.dtype)
+                segment_ids = torch.arange(len(target_lengths), device=target_lengths.device).repeat_interleave(target_lengths)
+                sequence_wise_mask = torch.zeros(len(target_lengths), dtype=target_lengths.dtype, device=token_wise_mask.device).scatter_add(0, segment_ids, token_wise_mask)
+                mask = mask * torch.ge(sequence_wise_mask, thres * target_lengths).to(flattened_targets.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.lm_head(hidden_states)
+            # log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, CausalLMOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
+                Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
+                the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
+                All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
+                config.vocab_size - 1]`.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None and labels.max() >= self.config.vocab_size:
+                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            hidden_states = self.dropout(hidden_states)
+
+            logits = self.lm_head(hidden_states)
+
+            loss = None
+            if labels is not None:
+                # retrieve loss input_lengths from attention_mask
+                attention_mask = (
+                    attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
+                )
+                input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+                shared_head = Wav2Vec2ForCTCConfig.gen_shared_head(self)
+                criterion = Wav2Vec2ForCTCConfig.gen_criterion(input_lengths, self.config.pad_token_id, self.config.ctc_zero_infinity)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states,  logits, labels, loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForCTCConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return CausalLMOutput(
+                loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+            )
+        return func
+
+
+# MBart for Translation
+class MBartForConditionalGenerationConfig(Config): 
+    @staticmethod
+    def gen_criterion(vocab_size: int, top_k=1):
+        def func(logits, labels, mask=None):
+            """
+            Args:
+                logits (Tensor): Logits tensor of shape [B, L, V].
+                labels (Tensor): Target labels of shape [B, L].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(labels.view(-1), dtype=torch.float32, device=labels.device)
+                
+            masked_losses = F.cross_entropy(logits.view(-1, vocab_size), labels.view(-1), reduction="none")
+            loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(logits.view(-1, vocab_size), top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, labels.view(-1, 1)).any(dim=1).to(logits.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, L].
+            """
+            logits = self.lm_head(hidden_states) + self.final_logits_bias
+            return logits
+        return func
+    
+    @staticmethod
+    def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+        """
+        Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
+        have a single `decoder_start_token_id` in contrast to other Bart-like models.
+        """
+        prev_output_tokens = input_ids.clone()
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+        index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+        decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+        prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+        prev_output_tokens[:, 0] = decoder_start_tokens
+
+        return prev_output_tokens
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            Returns:
+
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None:
+                # if use_cache:
+                #     logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+                use_cache = False
+                if decoder_input_ids is None and decoder_inputs_embeds is None:
+                    decoder_input_ids = MBartForConditionalGenerationConfig.shift_tokens_right(labels, self.config.pad_token_id)
+
+            outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=decoder_input_ids,
+                encoder_outputs=encoder_outputs,
+                decoder_attention_mask=decoder_attention_mask,
+                head_mask=head_mask,
+                decoder_head_mask=decoder_head_mask,
+                cross_attn_head_mask=cross_attn_head_mask,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+            masked_lm_loss = None
+            if labels is not None:
+                shared_head = MBartForConditionalGenerationConfig.gen_shared_head(self)
+                criterion = MBartForConditionalGenerationConfig.gen_criterion(self.config.vocab_size)
+                masked_lm_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, outputs[0],  lm_logits, labels, loss_normalization)
+
+            if not return_dict:
+                output = (lm_logits,) + outputs[1:]
+                return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+            return Seq2SeqLMOutput(
+                loss=masked_lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                decoder_hidden_states=outputs.decoder_hidden_states,
+                decoder_attentions=outputs.decoder_attentions,
+                cross_attentions=outputs.cross_attentions,
+                encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+                encoder_hidden_states=outputs.encoder_hidden_states,
+                encoder_attentions=outputs.encoder_attentions,
+            )
+        return func
+
+
+def apply_trp(model, depths: int, p: float, lambdas: List[float], **kwargs):
+    if isinstance(model, transformers.Wav2Vec2ForSequenceClassification):
+        print("✅ Applying TRP to Wav2Vec2 for Audio Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, MobileNetV2):
+        print("✅ Applying TRP to MobileNetV2 for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(MobileNetV2Config.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, ResNet):
+        print("✅ Applying TRP to ResNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p) for _ in lambdas])
+        model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, EfficientNet):
+        print("✅ Applying TRP to EfficientNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(EfficientNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, VisionTransformer):
+        print("✅ Applying TRP to VisionTransformer for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(VisionTransformerConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, transformers.BertForQuestionAnswering):
+        print("✅ Applying TRP to Bert for Question Answering...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForQuestionAnsweringConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, FCN):
+        print("✅ Applying TRP to FCN for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(FCNConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, DeepLabV3):
+        print("✅ Applying TRP to DeepLabV3 for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(DeepLabV3Config.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, transformers.BertForSequenceClassification):
+        print("✅ Applying TRP to Bert for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.RobertaForSequenceClassification):
+        print("✅ Applying TRP to Roberta for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(RobertaForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.Wav2Vec2ForCTC):
+        print("✅ Applying TRP to Wav2Vec2 for Speech Recognition...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForCTCConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.MBartForConditionalGeneration):
+        print("✅ Applying TRP to MBart for Translation...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(MBartForConditionalGenerationConfig.gen_forward(lambdas, False), model)
+    else:
+        torch._assert(
+            isinstance(model, transformers.Wav2Vec2ForSequenceClassification), 
+            "The model should be an object of [`Wav2Vec2ForSequenceClassification`].")
+
+    return model
\ No newline at end of file
diff --git a/hpo-examples/semantic-segmentation/utils.py b/hpo-examples/semantic-segmentation/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2fa1edb8cef956eb32dddb5592c70fe90755785
--- /dev/null
+++ b/hpo-examples/semantic-segmentation/utils.py
@@ -0,0 +1,300 @@
+import datetime
+import errno
+import os
+import time
+from collections import defaultdict, deque
+
+import torch
+import torch.distributed as dist
+
+
+class SmoothedValue:
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        t = reduce_across_processes([self.count, self.total])
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value
+        )
+
+
+class ConfusionMatrix:
+    def __init__(self, num_classes):
+        self.num_classes = num_classes
+        self.mat = None
+
+    def update(self, a, b):
+        n = self.num_classes
+        if self.mat is None:
+            self.mat = torch.zeros((n, n), dtype=torch.int64, device=a.device)
+        with torch.inference_mode():
+            k = (a >= 0) & (a < n)
+            inds = n * a[k].to(torch.int64) + b[k]
+            self.mat += torch.bincount(inds, minlength=n**2).reshape(n, n)
+
+    def reset(self):
+        self.mat.zero_()
+
+    def compute(self):
+        h = self.mat.float()
+        acc_global = torch.diag(h).sum() / h.sum()
+        acc = torch.diag(h) / h.sum(1)
+        iu = torch.diag(h) / (h.sum(1) + h.sum(0) - torch.diag(h))
+        return acc_global, acc, iu
+
+    def reduce_from_all_processes(self):
+        reduce_across_processes(self.mat)
+
+    def __str__(self):
+        acc_global, acc, iu = self.compute()
+        return ("global correct: {:.1f}\naverage row correct: {}\nIoU: {}\nmean IoU: {:.1f}").format(
+            acc_global.item() * 100,
+            [f"{i:.1f}" for i in (acc * 100).tolist()],
+            [f"{i:.1f}" for i in (iu * 100).tolist()],
+            iu.mean().item() * 100,
+        )
+
+
+class MetricLogger:
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            if not isinstance(v, (float, int)):
+                raise TypeError(
+                    f"This method expects the value of the input arguments to be of type float or int, instead  got {type(v)}"
+                )
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{attr}'")
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append(f"{name}: {str(meter)}")
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None):
+        i = 0
+        if not header:
+            header = ""
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt="{avg:.4f}")
+        data_time = SmoothedValue(fmt="{avg:.4f}")
+        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
+        if torch.cuda.is_available():
+            log_msg = self.delimiter.join(
+                [
+                    header,
+                    "[{0" + space_fmt + "}/{1}]",
+                    "eta: {eta}",
+                    "{meters}",
+                    "time: {time}",
+                    "data: {data}",
+                    "max mem: {memory:.0f}",
+                ]
+            )
+        else:
+            log_msg = self.delimiter.join(
+                [header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}"]
+            )
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB,
+                        )
+                    )
+                else:
+                    print(
+                        log_msg.format(
+                            i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time)
+                        )
+                    )
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print(f"{header} Total time: {total_time_str}")
+
+
+def cat_list(images, fill_value=0):
+    max_size = tuple(max(s) for s in zip(*[img.shape for img in images]))
+    batch_shape = (len(images),) + max_size
+    batched_imgs = images[0].new(*batch_shape).fill_(fill_value)
+    for img, pad_img in zip(images, batched_imgs):
+        pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img)
+    return batched_imgs
+
+
+def collate_fn(batch):
+    images, targets = list(zip(*batch))
+    batched_imgs = cat_list(images, fill_value=0)
+    batched_targets = cat_list(targets, fill_value=255)
+    return batched_imgs, batched_targets
+
+
+def mkdir(path):
+    try:
+        os.makedirs(path)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)
+
+
+def init_distributed_mode(args):
+    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ["WORLD_SIZE"])
+        args.gpu = int(os.environ["LOCAL_RANK"])
+    elif "SLURM_PROCID" in os.environ:
+        args.rank = int(os.environ["SLURM_PROCID"])
+        args.gpu = args.rank % torch.cuda.device_count()
+    elif hasattr(args, "rank"):
+        pass
+    else:
+        print("Not using distributed mode")
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = "nccl"
+    print(f"| distributed init (rank {args.rank}): {args.dist_url}", flush=True)
+    torch.distributed.init_process_group(
+        backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
+    )
+    torch.distributed.barrier()
+    setup_for_distributed(args.rank == 0)
+
+
+def reduce_across_processes(val):
+    if not is_dist_avail_and_initialized():
+        # nothing to sync, but we still convert to tensor for consistency with the distributed case.
+        return torch.tensor(val)
+
+    t = torch.tensor(val, device="cuda") if isinstance(val, int) else val.clone().detach().to("cuda")
+    dist.barrier()
+    dist.all_reduce(t)
+    return t
diff --git a/hpo-examples/speech-recognition/README.md b/hpo-examples/speech-recognition/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4990219f42a143a799201f6d044b624942dd08e8
--- /dev/null
+++ b/hpo-examples/speech-recognition/README.md
@@ -0,0 +1,617 @@
+<!---
+Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Automatic Speech Recognition Examples
+
+## Table of Contents
+
+- [Automatic Speech Recognition with CTC](#connectionist-temporal-classification)
+	- [Single GPU example](#single-gpu-ctc)
+	- [Multi GPU example](#multi-gpu-ctc)
+	- [Examples](#examples-ctc)
+		- [TIMIT](#timit-ctc)
+		- [Librispeech](#librispeech-ctc)
+		- [Common Voice](#common-voice-ctc)
+		- [Multilingual Librispeech](#multilingual-librispeech-ctc)
+- [Automatic Speech Recognition with CTC and Adapter Layers](#connectionist-temporal-classification-with-adapters)
+	- [Massive Multilingual Speech (MMS)](#mms-model)
+	- [Examples](#examples-ctc-adapter)
+		- [Common Voice](#common-voice-ctc-adapter)
+- [Automatic Speech Recognition with Sequence-to-Sequence](#sequence-to-sequence)
+	- [Whisper Model](#whisper-model)
+	- [Speech-Encoder-Decoder Model](#warm-started-speech-encoder-decoder-model)
+	- [Examples](#examples-seq2seq)
+		- [Librispeech](#librispeech-seq2seq)
+
+## Connectionist Temporal Classification
+
+The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForCTC) for automatic speech 
+recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
+
+Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only 
+very little annotated data to yield good performance on automatic speech recognition datasets.
+
+In the script [`run_speech_recognition_ctc`], we first create a vocabulary from all unique characters of both the training data and evaluation data. Then, we preprocesses the speech recognition dataset, which includes correct resampling, normalization and padding. Finally, the pretrained speech recognition model is fine-tuned on the annotated speech recognition datasets using CTC loss.
+
+---
+**NOTE**
+
+If you encounter problems with data preprocessing by setting `--preprocessing_num_workers` > 1, 
+you might want to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
+
+```bash
+OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
+```
+
+If the environment variable is not set, the training script might freeze, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239
+
+---
+
+### Single GPU CTC
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
+
+```bash
+python run_speech_recognition_ctc.py \
+	--dataset_name="common_voice" \
+	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+	--dataset_config_name="tr" \
+	--output_dir="./wav2vec2-common_voice-tr-demo" \
+	--overwrite_output_dir \
+	--num_train_epochs="15" \
+	--per_device_train_batch_size="16" \
+	--gradient_accumulation_steps="2" \
+	--learning_rate="3e-4" \
+	--warmup_steps="500" \
+	--eval_strategy="steps" \
+	--text_column_name="sentence" \
+	--length_column_name="input_length" \
+	--save_steps="400" \
+	--eval_steps="100" \
+	--layerdrop="0.0" \
+	--save_total_limit="3" \
+	--freeze_feature_encoder \
+	--gradient_checkpointing \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--fp16 \
+	--group_by_length \
+	--push_to_hub \
+	--do_train --do_eval 
+```
+
+On a single V100 GPU, this script should run in *ca.* 1 hour 20 minutes and yield a CTC loss of **0.39** and word error rate
+of **0.35**.
+
+### Multi GPU CTC
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
+
+```bash
+torchrun \
+	--nproc_per_node 8 run_speech_recognition_ctc.py \
+	--dataset_name="common_voice" \
+	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+	--dataset_config_name="tr" \
+	--output_dir="./wav2vec2-common_voice-tr-demo-dist" \
+	--overwrite_output_dir \
+	--num_train_epochs="15" \
+	--per_device_train_batch_size="4" \
+	--learning_rate="3e-4" \
+	--warmup_steps="500" \
+	--eval_strategy="steps" \
+	--text_column_name="sentence" \
+	--length_column_name="input_length" \
+	--save_steps="400" \
+	--eval_steps="100" \
+	--logging_steps="1" \
+	--layerdrop="0.0" \
+	--save_total_limit="3" \
+	--freeze_feature_encoder \
+	--gradient_checkpointing \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--fp16 \
+	--group_by_length \
+	--push_to_hub \
+	--do_train --do_eval
+```
+
+On 8 V100 GPUs, this script should run in *ca.* 18 minutes and yield a CTC loss of **0.39** and word error rate
+of **0.36**.
+
+
+### Multi GPU CTC with Dataset Streaming
+
+The following command shows how to use [Dataset Streaming mode](https://huggingface.co/docs/datasets/dataset_streaming)
+to fine-tune [XLS-R](https://huggingface.co/transformers/main/model_doc/xls_r.html) 
+on [Common Voice](https://huggingface.co/datasets/common_voice) using 4 GPUs in half-precision.
+
+Streaming mode imposes several constraints on training:
+1. We need to construct a tokenizer beforehand and define it via `--tokenizer_name_or_path`.
+2. `--num_train_epochs` has to be replaced by `--max_steps`. Similarly, all other epoch-based arguments have to be 
+replaced by step-based ones.
+3. Full dataset shuffling on each epoch is not possible, since we don't have the whole dataset available at once. 
+However, the `--shuffle_buffer_size` argument controls how many examples we can pre-download before shuffling them.
+
+
+```bash
+**torchrun \
+	--nproc_per_node 4 run_speech_recognition_ctc_streaming.py \
+	--dataset_name="common_voice" \
+	--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
+	--tokenizer_name_or_path="anton-l/wav2vec2-tokenizer-turkish" \
+	--dataset_config_name="tr" \
+	--train_split_name="train+validation" \
+	--eval_split_name="test" \
+	--output_dir="wav2vec2-xls-r-common_voice-tr-ft" \
+	--overwrite_output_dir \
+	--max_steps="5000" \
+	--per_device_train_batch_size="8" \
+	--gradient_accumulation_steps="2" \
+	--learning_rate="5e-4" \
+	--warmup_steps="500" \
+	--eval_strategy="steps" \
+	--text_column_name="sentence" \
+	--save_steps="500" \
+	--eval_steps="500" \
+	--logging_steps="1" \
+	--layerdrop="0.0" \
+	--eval_metrics wer cer \
+	--save_total_limit="1" \
+	--mask_time_prob="0.3" \
+	--mask_time_length="10" \
+	--mask_feature_prob="0.1" \
+	--mask_feature_length="64" \
+	--freeze_feature_encoder \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--max_duration_in_seconds="20" \
+	--shuffle_buffer_size="500" \
+	--fp16 \
+	--push_to_hub \
+	--do_train --do_eval \
+	--gradient_checkpointing**
+```
+
+On 4 V100 GPUs, this script should run in *ca.* 3h 31min and yield a CTC loss of **0.35** and word error rate
+of **0.29**.
+
+### Examples CTC
+
+The following tables present a couple of example runs on the most popular speech-recognition datasets. 
+The presented performances are by no means optimal as no hyper-parameter tuning was done. Nevertheless, 
+they can serve as a baseline to improve upon.
+
+
+#### TIMIT CTC
+
+- [TIMIT](https://huggingface.co/datasets/timit_asr)
+
+| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
+|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
+| [TIMIT](https://huggingface.co/datasets/timit_asr)| -  | [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 0.21 | - | 1 GPU TITAN RTX |  32min                      | [here](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned)  | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned/blob/main/run.sh) |
+| [TIMIT](https://huggingface.co/datasets/timit_asr)| -  | [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 0.21 | - | 1 GPU TITAN RTX |  32min                      | [here](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned)  | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned/blob/main/run.sh) |
+| [TIMIT](https://huggingface.co/datasets/timit_asr)| -  | [unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) | 0.22 | - | 1 GPU TITAN RTX |  35min                      | [here](https://huggingface.co/patrickvonplaten/unispeech-large-1500h-cv-timit)  | [run.sh](https://huggingface.co/patrickvonplaten/unispeech-large-1500h-cv-timit/blob/main/run.sh) |
+| [TIMIT](https://huggingface.co/datasets/timit_asr)| -  | [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.30 | - | 1 GPU TITAN RTX |  28min                      | [here](https://huggingface.co/patrickvonplaten/sew-small-100k-timit)  | [run.sh](https://huggingface.co/patrickvonplaten/sew-small-100k-timit/blob/main/run.sh) |
+| [TIMIT](https://huggingface.co/datasets/timit_asr)| -  | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 0.68 | - | 1 GPU TITAN RTX |  26min                      | [here](https://huggingface.co/patrickvonplaten/distilhubert-timit)  | [run.sh](https://huggingface.co/patrickvonplaten/distilhubert-timit/blob/main/run.sh) |
+
+
+#### Librispeech CTC
+
+- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
+
+| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
+|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) | 0.049 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-large) | [run.sh](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-large/blob/main/run.sh) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) | 0.068 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus) | [run.sh](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus/blob/main/run.sh) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) | 0.088 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.167 | | 8 GPU V100 | 54min  | [here](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft) | [run.sh](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft/blob/main/run.sh) |
+
+
+#### Common Voice CTC
+
+- [Common Voice](https://huggingface.co/datasets/common_voice)
+
+| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
+|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
+| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"tr"`  | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | - |  0.099   | 8 GPU V100   |  23min                 | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-tr-phoneme)      |  [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-tr-phoneme/blob/main/run.sh) |
+| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"it"`  | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | - |  0.077   | 8 GPU V100   |  23min                 | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-it-phoneme)      |  [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-it-phoneme/blob/main/run.sh) |
+| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"sv-SE"`  | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | - |  0.099   | 8 GPU V100   |  23min                 | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-sv-phoneme)      |  [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-sv-phoneme/blob/main/run.sh) |
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)  | 0.36 |  -      | 8 GPU V100   |  18min                 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo-dist)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo-dist/blob/main/run_dist.sh) |
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)  | 0.31  | -    | 8 GPU V100   |  1h05                 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-common_voice-tr-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-common_voice-tr-ft/blob/main/run.sh) |
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.35 | - | 1 GPU V100   |  1h20min                      | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo)  | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo/blob/main/run.sh) |
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | 0.31     | - | 8 GPU V100   |  1h05            | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft/blob/main/run.sh) |
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b)  | 0.21 | -  | 2 GPU Titan 24 GB RAM   |  15h10            | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xls-r-1b-common_voice-tr-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-1b-common_voice-tr-ft/blob/main/run.sh) |
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` in streaming mode  | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | 0.29     | - | 4 GPU V100   |  3h31            | [here](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream)      |  [run.sh](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream/blob/main/run.sh) |
+
+
+#### Multilingual Librispeech CTC
+
+- [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)
+
+| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
+|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
+| [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"`  | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)  | 0.13  | -     | 1 GPU Titan 24 GB RAM  |  15h04                 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft/blob/main/run.sh) |
+| [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"`  | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | 0.15 | -     | 1 GPU Titan 24 GB RAM  |  15h04                 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft/blob/main/run.sh) |
+
+## Connectionist Temporal Classification With Adapters
+
+The script [`run_speech_recognition_ctc_adapter.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py) can be used to fine-tune adapter layers for [Wav2Vec2-like models like MMS](https://huggingface.co/docs/transformers/main/en/model_doc/mms) for automatic speech recognition.
+
+### MMS Model
+
+The [Massive Multilingual Speech (MMS) model](https://huggingface.co/facebook/mms-1b-all) has been pre-trained and fine-tuned
+on 1000+ languages. The model makes use of adapter attention layers to fine-tune only a small part 
+of the model on a specific language. The model already comes with fine-tuned adapter layers for 1000+ languages and 
+can be used for inference for 1000+ languages out of the box.
+
+However, for improved performance or more specific use cases one can re-initialize the adapter weights, freeze all 
+other weights and fine-tune them on a specific dataset as shown in the [example below](#examples-ctc-adapter).
+
+Note that the adapter weights include low dimensional linear layers for every attention block as well as the final language
+model head layers.
+
+### Examples CTC Adapter
+
+In the following we will look at how one can fine-tune adapter weights for any of the 
+[MMS CTC checkpoints](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&other=mms&sort=downloads) in less than 1 hour.
+
+#### Common Voice CTC Adapter
+
+As in the examples [above](#examples-ctc), we fine-tune on Common Voice's 6 dataset in Turkish as an example.
+Contrary to [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) before there is a `--target_language` which has to be defined to state for which 
+language or concept the adapter layers shall be trained. The adapter weights will then 
+accordingly be called `adapter.{<target_language}.safetensors`.
+
+Let's run an example script. Make sure to be logged in so that your model can be directly uploaded to the Hub.
+```bash
+huggingface-cli login
+```
+
+Now, let's run an example and upload it to the Hub under `wav2vec2-common_voice-tr-mms-demo`.
+
+```sh
+python run_speech_recognition_ctc.py \
+	--dataset_name="common_voice" \
+	--model_name_or_path="facebook/mms-1b-all" \
+	--dataset_config_name="tr" \
+	--output_dir="./wav2vec2-common_voice-tr-mms-demo" \
+	--num_train_epochs="4" \
+	--per_device_train_batch_size="32" \
+	--learning_rate="1e-3" \
+	--warmup_steps="100" \
+	--eval_strategy="steps" \
+	--text_column_name="sentence" \
+	--length_column_name="input_length" \
+	--save_steps="200" \
+	--eval_steps="100" \
+	--save_total_limit="3" \
+  --target_language="tur" \
+	--gradient_checkpointing \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--fp16 \
+	--group_by_length \
+	--do_train --do_eval \
+  --push_to_hub
+```
+
+This should take less than 10 minutes on most GPUs and you should very quickly get word error rates 
+below 27%.
+
+For an example run, you can have a look at [`patrickvonplaten/wav2vec2-common_voice-tr-mms-demo`](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-mms-demo).
+
+
+If you'd like to train another adapter model with the same base model, you can simply re-use the same `--output_dir`,
+but make sure to pass the `--output_dir` folder also to `--tokenizer_name_or_path` so that the vocabulary is not 
+overwritten but **extended**. Assuming you would like to train adapter weights on Swedish in addition to Turkish and save 
+the adapter weights in the same model repo, you can run:
+
+```sh
+python run_speech_recognition_ctc.py \
+	--dataset_name="common_voice" \
+	--model_name_or_path="facebook/mms-1b-all" \
+	--dataset_config_name="sw" \
+	--output_dir="./wav2vec2-common_voice-tr-mms-demo" \
+	--tokenizer_name_or_path="./wav2vec2-common_voice-tr-mms-demo" \
+	--num_train_epochs="4" \
+	--per_device_train_batch_size="32" \
+	--learning_rate="1e-3" \
+	--warmup_steps="100" \
+	--eval_strategy="steps" \
+	--text_column_name="sentence" \
+	--length_column_name="input_length" \
+	--save_steps="200" \
+	--eval_steps="100" \
+	--save_total_limit="3" \
+  --target_language="swe" \
+	--gradient_checkpointing \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--fp16 \
+	--group_by_length \
+	--do_train --do_eval \
+  --push_to_hub
+```
+
+Now you should have both `adapter.tur.safetensors` and `adapter.swe.safetensors` in the model repo
+and you can load the respective language with:
+```py
+model.load_adapter("tur")  # or "swe"
+```
+respectively.
+
+## Sequence to Sequence
+
+The script [`run_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) can be used to fine-tune any [Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForSpeechSeq2Seq) for automatic speech 
+recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset. This includes the Whisper model from OpenAI or a warm-started Speech-Encoder-Decoder Model, examples for which are included below.
+
+### Whisper Model
+We can load all components of the Whisper model directly from the pretrained checkpoint, including the pretrained model weights, feature extractor and tokenizer. We simply have to specify our fine-tuning dataset and training hyperparameters.
+
+#### Single GPU Whisper Training
+The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint on the Hindi subset of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) using a single GPU device in half-precision:
+```bash
+python run_speech_recognition_seq2seq.py \
+	--model_name_or_path="openai/whisper-small" \
+	--dataset_name="mozilla-foundation/common_voice_11_0" \
+	--dataset_config_name="hi" \
+	--language="hindi" \
+	--task="transcribe" \
+	--train_split_name="train+validation" \
+	--eval_split_name="test" \
+	--max_steps="5000" \
+	--output_dir="./whisper-small-hi" \
+	--per_device_train_batch_size="16" \
+	--gradient_accumulation_steps="2" \
+	--per_device_eval_batch_size="16" \
+	--logging_steps="25" \
+	--learning_rate="1e-5" \
+	--warmup_steps="500" \
+	--eval_strategy="steps" \
+	--eval_steps="1000" \
+	--save_strategy="steps" \
+	--save_steps="1000" \
+	--generation_max_length="225" \
+	--preprocessing_num_workers="16" \
+	--max_duration_in_seconds="30" \
+	--text_column_name="sentence" \
+	--freeze_feature_encoder="False" \
+	--gradient_checkpointing \
+	--fp16 \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--predict_with_generate \
+	--use_auth_token
+```
+On a single V100, training should take approximately 8 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
+
+If training on a different language, you should be sure to change the `language` argument. The `language` and `task` 
+arguments should be omitted for English speech recognition.
+
+#### Multi GPU Whisper Training
+The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint on the Hindi subset of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) using 2 GPU devices in half-precision:
+```bash
+torchrun \
+ 	--nproc_per_node 2 run_speech_recognition_seq2seq.py \
+	--model_name_or_path="openai/whisper-small" \
+	--dataset_name="mozilla-foundation/common_voice_11_0" \
+	--dataset_config_name="hi" \
+	--language="hindi" \
+	--task="transcribe" \
+	--train_split_name="train+validation" \
+	--eval_split_name="test" \
+	--max_steps="5000" \
+	--output_dir="./whisper-small-hi" \
+	--per_device_train_batch_size="16" \
+	--per_device_eval_batch_size="16" \
+	--logging_steps="25" \
+	--learning_rate="1e-5" \
+	--warmup_steps="500" \
+	--eval_strategy="steps" \
+	--eval_steps="1000" \
+	--save_strategy="steps" \
+	--save_steps="1000" \
+	--generation_max_length="225" \
+	--preprocessing_num_workers="16" \
+	--max_duration_in_seconds="30" \
+	--text_column_name="sentence" \
+	--freeze_feature_encoder="False" \
+	--gradient_checkpointing \
+	--fp16 \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--predict_with_generate \
+	--use_auth_token
+```
+On two V100s, training should take approximately 4 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
+
+### Warm-Started Speech-Encoder-Decoder Model
+A very common use case is to leverage a pretrained speech encoder model,
+*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html) or [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), with a pretrained text decoder model, *e.g.* [BART](https://huggingface.co/docs/transformers/main/en/model_doc/bart#transformers.BartForCausalLM) or [GPT-2](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForCausalLM), to create a [Speech-Encoder-Decoder Model](https://huggingface.co/docs/transformers/main/en/model_doc/speech-encoder-decoder#speech-encoder-decoder-models).
+
+By pairing a pretrained speech model with a pretrained text model, the warm-started model has prior knowledge of both the source audio and target text domains. However, the cross-attention weights between the encoder and decoder are randomly initialised. Thus, the model requires fine-tuning to learn the cross-attention weights and align the encoder mapping with that of the decoder. We can perform this very fine-tuning procedure using the example script.
+
+As an example, let's instantiate a *Wav2Vec2-2-Bart* model with the `SpeechEncoderDecoderModel` framework. First create an empty repo on `hf.co`:
+
+```bash
+huggingface-cli repo create wav2vec2-2-bart-base
+git clone https://huggingface.co/<your-user-name>/wav2vec2-2-bart-base
+cd wav2vec2-2-bart-base
+```
+
+Next, run the following script **inside** the just cloned repo:
+
+```python
+from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2Processor
+
+# checkpoints to leverage
+encoder_id = "facebook/wav2vec2-base"
+decoder_id = "facebook/bart-base"
+
+# load and save speech-encoder-decoder model
+# set some hyper-parameters for training and evaluation
+model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True, encoder_feat_proj_dropout=0.0, encoder_layerdrop=0.0, max_length=200, num_beams=5)
+model.config.decoder_start_token_id = model.decoder.config.bos_token_id
+model.config.pad_token_id = model.decoder.config.pad_token_id
+model.config.eos_token_id = model.decoder.config.eos_token_id
+model.save_pretrained("./")
+
+# load and save processor
+feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
+tokenizer = AutoTokenizer.from_pretrained(decoder_id)
+processor = Wav2Vec2Processor(feature_extractor, tokenizer)
+processor.save_pretrained("./")
+```
+
+Finally, we can upload all files:
+```bash
+git lfs install
+git add . && git commit -m "upload model files" && git push
+```
+
+and link the official `run_speech_recognition_seq2seq.py` script to the folder:
+
+```bash
+ln -s $(realpath <path/to/transformers>/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) ./
+```
+
+Note that we have added a randomly initialized _adapter layer_ to `wav2vec2-base` with the argument
+`encoder_add_adapter=True`. This adapter sub-samples the output sequence of 
+`wav2vec2-base` along the time dimension. By default, a single
+output vector of `wav2vec2-base` has a receptive field of *ca.* 25ms (*cf.* 
+Section *4.2* of the [official Wav2Vec2 paper](https://arxiv.org/pdf/2006.11477.pdf)), which represents a little less a single character. On the other hand, BART
+makes use of a sentence-piece tokenizer as an input processor, so that a single 
+hidden vector of `bart-base` represents *ca.* 4 characters. To better align the 
+receptive field of the *Wav2Vec2* output vectors with *BART*'s hidden-states in the cross-attention 
+mechanism, we further subsample *Wav2Vec2*'s output by a factor of 8 by 
+adding a convolution-based adapter.
+
+Having warm-started the speech-encoder-decoder model under `<your-user-name>/wav2vec2-2-bart`, we can now fine-tune it on the task of speech recognition.
+
+In the script [`run_speech_recognition_seq2seq`], we load the warm-started model, 
+feature extractor, and tokenizer, process a speech recognition dataset, 
+and subsequently make use of the [`Seq2SeqTrainer`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Seq2SeqTrainer) to train our system.
+Note that it is important to align the target transcriptions with the decoder's vocabulary. For example, the [`Librispeech`](https://huggingface.co/datasets/librispeech_asr) dataset only contains capitalized letters in the transcriptions,
+whereas BART was pretrained mostly on normalized text. Thus, it is recommended to add the argument 
+`--do_lower_case` to the fine-tuning script when using a warm-started `SpeechEncoderDecoderModel`. 
+The model is fine-tuned on the standard cross-entropy language modeling
+loss for sequence-to-sequence (just like *T5* or *BART* in natural language processing).
+
+---
+**NOTE**
+
+If you encounter problems with data preprocessing by setting `--preprocessing_num_workers` > 1, 
+you might want to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
+
+```bash
+OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
+```
+
+If the environment variable is not set, the training script might freeze, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239.
+
+---
+
+#### Single GPU Seq2Seq
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
+
+```bash
+python run_speech_recognition_seq2seq.py \
+	--dataset_name="librispeech_asr" \
+	--model_name_or_path="./" \
+	--dataset_config_name="clean" \
+	--train_split_name="train.100" \
+	--eval_split_name="validation" \
+	--output_dir="./" \
+	--preprocessing_num_workers="16" \
+	--length_column_name="input_length" \
+	--overwrite_output_dir \
+	--num_train_epochs="5" \
+	--per_device_train_batch_size="8" \
+	--per_device_eval_batch_size="8" \
+	--gradient_accumulation_steps="8" \
+	--learning_rate="3e-4" \
+	--warmup_steps="400" \
+	--eval_strategy="steps" \
+	--text_column_name="text" \
+	--save_steps="400" \
+	--eval_steps="400" \
+	--logging_steps="10" \
+	--save_total_limit="1" \
+	--freeze_feature_encoder \
+	--gradient_checkpointing \
+	--fp16 \
+	--group_by_length \
+	--predict_with_generate \
+	--generation_max_length="40" \
+	--generation_num_beams="1" \
+	--do_train --do_eval \
+	--do_lower_case
+```
+
+On a single V100 GPU, this script should run in *ca.* 5 hours and yield a 
+cross-entropy loss of **0.405** and word error rate of **0.0728**.
+
+#### Multi GPU Seq2Seq
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
+
+```bash
+torchrun \
+ 	--nproc_per_node 8 run_speech_recognition_seq2seq.py \
+	--dataset_name="librispeech_asr" \
+	--model_name_or_path="./" \
+	--dataset_config_name="clean" \
+	--train_split_name="train.100" \
+	--eval_split_name="validation" \
+	--output_dir="./" \
+	--preprocessing_num_workers="16" \
+	--length_column_name="input_length" \
+	--overwrite_output_dir \
+	--num_train_epochs="5" \
+	--per_device_train_batch_size="8" \
+	--per_device_eval_batch_size="8" \
+	--gradient_accumulation_steps="1" \
+	--learning_rate="3e-4" \
+	--warmup_steps="400" \
+	--eval_strategy="steps" \
+	--text_column_name="text" \
+	--save_steps="400" \
+	--eval_steps="400" \
+	--logging_steps="10" \
+	--save_total_limit="1" \
+	--freeze_feature_encoder \
+	--gradient_checkpointing \
+	--fp16 \
+	--group_by_length \
+	--predict_with_generate \
+	--do_train --do_eval \
+	--do_lower_case
+```
+
+On 8 V100 GPUs, this script should run in *ca.* 45 minutes and yield a cross-entropy loss of **0.405** and word error rate of **0.0728**
+
+### Examples Seq2Seq
+
+#### Librispeech Seq2Seq
+
+- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
+
+| Dataset                                                        | Dataset Config            | Pretrained Model                                                                                                                                          | Word error rate on eval | Phoneme error rate on eval | GPU setup  | Training time | Fine-tuned Model & Logs                                               | Command to reproduce                                                                                                                                                                                              |
+|----------------------------------------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|----------------------------|------------|---------------|-----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr) | `"clean"` - `"train.100"` | [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) and [facebook/bart-base](https://huggingface.co/facebook/bart-base)               | 0.0728                  | -                          | 8 GPU V100 | 45min         | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base)  | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/run_librispeech.sh)   |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr) | `"clean"` - `"train.100"` | [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) and [facebook/bart-large](https://huggingface.co/facebook/bart-large) | 0.0486                  | -                          | 8 GPU V100 | 1h20min       | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large) | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/run_librispeech.sh) |
diff --git a/hpo-examples/speech-recognition/requirements.txt b/hpo-examples/speech-recognition/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a16697b038c6350b32ee9e35b6fd6331ee1e26da
--- /dev/null
+++ b/hpo-examples/speech-recognition/requirements.txt
@@ -0,0 +1,6 @@
+datasets >= 1.18.0
+torch >= 1.5
+torchaudio
+librosa
+jiwer
+evaluate
diff --git a/hpo-examples/speech-recognition/run.sh b/hpo-examples/speech-recognition/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..79175edcaba6aea2dcea8a811fb30e675fb79cde
--- /dev/null
+++ b/hpo-examples/speech-recognition/run.sh
@@ -0,0 +1,92 @@
+# CUDA_VISIBLE_DEVICES="0" python run_speech_recognition_ctc.py \
+#         --dataset_name="timit_asr" \
+#         --model_name_or_path="facebook/wav2vec2-base" \
+#         --overwrite_output_dir \
+#         --output_dir="./wav2vec2-base-timit-fine-tuned" \
+#         --train_split_name="train" \
+#         --num_train_epochs="20" \
+#         --per_device_train_batch_size="32" \
+#         --per_device_eval_batch_size="1" \
+#         --weight_decay="0.005" \
+#         --learning_rate="1e-4" \
+#         --warmup_steps="1000" \
+#         --evaluation_strategy="steps" \
+#         --text_column_name="text" \
+#         --save_steps="400" \
+#         --eval_steps="100" \
+#         --logging_steps="10" \
+#         --layerdrop="0.0" \
+#         --save_total_limit="3" \
+#         --freeze_feature_extractor \
+#         --chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+#         --fp16 \
+#         --group_by_length \
+#         --push_to_hub \
+#         --do_train \
+#         --do_eval 
+
+
+# 
+CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_ctc.py \
+	--dataset_name="mozilla-foundation/common_voice_13_0" \
+	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+	--dataset_config_name="tr" \
+	--output_dir="./wav2vec2-common_voice-tr-demo" \
+	--preprocessing_num_workers="1" \
+	--overwrite_output_dir \
+	--num_train_epochs="15" \
+	--per_device_train_batch_size="16" \
+	--gradient_accumulation_steps="2" \
+	--learning_rate="3e-4" \
+	--warmup_steps="500" \
+	--evaluation_strategy="steps" \
+	--text_column_name="sentence" \
+	--save_steps="400" \
+	--eval_steps="100" \
+	--logging_steps="1" \
+	--layerdrop="0.0" \
+	--save_total_limit="3" \
+	--freeze_feature_encoder \
+	--gradient_checkpointing \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--fp16 \
+	--group_by_length \
+	--push_to_hub \
+	--do_train \
+    --do_eval \
+    --trust-remote-code \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+
+
+# CUDA_VISIBLE_DEVICES="0" python run_speech_recognition_ctc.py \
+#     --dataset_name="multilingual_librispeech" \
+#     --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+#     --train_split_name="train.9h" \
+#     --dataset_config_name="german" \
+#     --output_dir="./wav2vec2-xlsr-53-300m-mls-german-ft" \
+#     --overwrite_output_dir \
+#     --num_train_epochs="100" \
+#     --per_device_train_batch_size="32" \
+#     --gradient_accumulation_steps="1" \
+#     --learning_rate="1e-4" \
+#     --activation_dropout="0.1" \
+#     --warmup_steps="1000" \
+#     --evaluation_strategy="steps" \
+#     --text_column_name="text" \
+#     --save_steps="1000" \
+#     --eval_steps="500" \
+#     --logging_steps="10" \
+#     --layerdrop="0.1" \
+#     --hidden_dropout="0.0" \
+#     --save_total_limit="1" \
+#     --mask_time_prob="0.75" \
+#     --mask_feature_prob="0.25" \
+#     --mask_feature_length="64" \
+#     --freeze_feature_extractor \
+#     --fp16 \
+#     --group_by_length \
+#     --do_eval \
+#     --gradient_checkpointing \
+#     --use_auth_token \
+#     --do_train --do_eval \
+#     --push_to_hub \
\ No newline at end of file
diff --git a/hpo-examples/speech-recognition/run_speech_recognition_ctc.py b/hpo-examples/speech-recognition/run_speech_recognition_ctc.py
new file mode 100644
index 0000000000000000000000000000000000000000..f96fc14f5872a135a25cea51906aa6cf37217093
--- /dev/null
+++ b/hpo-examples/speech-recognition/run_speech_recognition_ctc.py
@@ -0,0 +1,851 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition"""
+
+import functools
+import json
+import logging
+import os
+import re
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import torch
+from datasets import DatasetDict, load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForCTC,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    Wav2Vec2Processor,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+from trplib import apply_trp
+
+
+# # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+# check_min_version("4.50.0.dev0")
+
+require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+
+logger = logging.getLogger(__name__)
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    tokenizer_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    attention_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
+    )
+    activation_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
+    )
+    feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
+    hidden_dropout: float = field(
+        default=0.0,
+        metadata={
+            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
+        },
+    )
+    final_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout probability for the final projection layer."},
+    )
+    mask_time_prob: float = field(
+        default=0.05,
+        metadata={
+            "help": (
+                "Probability of each feature vector along the time axis to be chosen as the start of the vector "
+                "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature "
+                "vectors will be masked along the time axis."
+            )
+        },
+    )
+    mask_time_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the time axis."},
+    )
+    mask_feature_prob: float = field(
+        default=0.0,
+        metadata={
+            "help": (
+                "Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan"
+                " to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature"
+                " bins will be masked along the time axis."
+            )
+        },
+    )
+    mask_feature_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the feature axis."},
+    )
+    layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
+    ctc_loss_reduction: Optional[str] = field(
+        default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
+    )
+    ctc_zero_infinity: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly"
+            " occur when the inputs are too short to be aligned to the targets."
+        },
+    )
+    add_adapter: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether a convolutional attention network should be stacked on top of the Wav2Vec2Bert Encoder. Can be very"
+            "useful to downsample the output length."
+        },
+    )
+
+    apply_trp: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to apply TRP or not."},
+    )
+    trp_depths: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "TRP depth value."
+        },
+    )
+    trp_p: Optional[float] = field(
+        default=0.1,
+        metadata={
+            "help": "TRP p value."
+        },
+    )
+    trp_lambdas: Optional[List[float]] = field(
+        default_factory=lambda: [0.4, 0.2, 0.1],
+        metadata={
+            "help": "TRP lambda values (list of floats)."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: str = field(
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: str = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_split_name: str = field(
+        default="train+validation",
+        metadata={
+            "help": (
+                "The name of the training data set split to use (via the datasets library). Defaults to "
+                "'train+validation'"
+            )
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'test'"
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of validation examples to this "
+                "value if set."
+            )
+        },
+    )
+    chars_to_ignore: Optional[List[str]] = list_field(
+        default=None,
+        metadata={"help": "A list of characters to remove from the transcripts."},
+    )
+    eval_metrics: List[str] = list_field(
+        default=["wer"],
+        metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Filter audio files that are longer than `max_duration_in_seconds` seconds to"
+                " 'max_duration_in_seconds`"
+            )
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is especially useful when data"
+                " preprocessing errors out in distributed training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
+                " can consequently be loaded in distributed training"
+            )
+        },
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    unk_token: str = field(
+        default="[UNK]",
+        metadata={"help": "The unk token for the tokenizer"},
+    )
+    pad_token: str = field(
+        default="[PAD]",
+        metadata={"help": "The padding token for the tokenizer"},
+    )
+    word_delimiter_token: str = field(
+        default="|",
+        metadata={"help": "The word delimiter token for the tokenizer"},
+    )
+    phoneme_language: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The target language that should be used be"
+                " passed to the tokenizer for tokenization. Note that"
+                " this is only relevant if the model classifies the"
+                " input audio to a sequence of phoneme sequences."
+            )
+        },
+    )
+
+
+@dataclass
+class DataCollatorCTCWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor (:class:`~transformers.AutoProcessor`)
+            The processor used for proccessing the data.
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        max_length (:obj:`int`, `optional`):
+            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
+        max_length_labels (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` returned list and optionally padding length (see above).
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
+            7.5 (Volta).
+    """
+
+    processor: AutoProcessor
+    padding: Union[bool, str] = "longest"
+    pad_to_multiple_of: Optional[int] = None
+    pad_to_multiple_of_labels: Optional[int] = None
+    feature_extractor_input_name: Optional[str] = "input_values"
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        input_features = [
+            {self.feature_extractor_input_name: feature[self.feature_extractor_input_name]} for feature in features
+        ]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.pad(
+            input_features,
+            padding=self.padding,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+        labels_batch = self.processor.pad(
+            labels=label_features,
+            padding=self.padding,
+            pad_to_multiple_of=self.pad_to_multiple_of_labels,
+            return_tensors="pt",
+        )
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        batch["labels"] = labels
+        if "attention_mask" in batch:
+            batch["attention_mask"] = batch["attention_mask"].to(torch.long)
+
+        return batch
+
+
+def create_vocabulary_from_data(
+    datasets: DatasetDict,
+    word_delimiter_token: Optional[str] = None,
+    unk_token: Optional[str] = None,
+    pad_token: Optional[str] = None,
+):
+    # Given training and test labels create vocabulary
+    def extract_all_chars(batch):
+        all_text = " ".join(batch["target_text"])
+        vocab = list(set(all_text))
+        return {"vocab": [vocab], "all_text": [all_text]}
+
+    vocabs = datasets.map(
+        extract_all_chars,
+        batched=True,
+        batch_size=-1,
+        keep_in_memory=True,
+        remove_columns=datasets["train"].column_names,
+    )
+
+    # take union of all unique characters in each dataset
+    vocab_set = functools.reduce(
+        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
+    )
+
+    vocab_dict = {v: k for k, v in enumerate(sorted(vocab_set))}
+
+    # replace white space with delimiter token
+    if word_delimiter_token is not None:
+        vocab_dict[word_delimiter_token] = vocab_dict[" "]
+        del vocab_dict[" "]
+
+    # add unk and pad token
+    if unk_token is not None:
+        vocab_dict[unk_token] = len(vocab_dict)
+
+    if pad_token is not None:
+        vocab_dict[pad_token] = len(vocab_dict)
+
+    return vocab_dict
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_speech_recognition_ctc", model_args, data_args)
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 1. First, let's load the dataset
+    raw_datasets = DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            token=data_args.token,
+            trust_remote_code=data_args.trust_remote_code,
+        )
+
+        if data_args.audio_column_name not in raw_datasets["train"].column_names:
+            raise ValueError(
+                f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'."
+                " Make sure to set `--audio_column_name` to the correct audio column - one of"
+                f" {', '.join(raw_datasets['train'].column_names)}."
+            )
+
+        if data_args.text_column_name not in raw_datasets["train"].column_names:
+            raise ValueError(
+                f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+                "Make sure to set `--text_column_name` to the correct text column - one of "
+                f"{', '.join(raw_datasets['train'].column_names)}."
+            )
+
+        if data_args.max_train_samples is not None:
+            raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            token=data_args.token,
+            trust_remote_code=data_args.trust_remote_code,
+        )
+
+        if data_args.max_eval_samples is not None:
+            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+
+    # 2. We remove some special characters from the datasets
+    # that make training complicated and do not help in transcribing the speech
+    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
+    # that could be easily picked up by the model
+    chars_to_ignore_regex = (
+        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
+    )
+    text_column_name = data_args.text_column_name
+
+    def remove_special_characters(batch):
+        if chars_to_ignore_regex is not None:
+            batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
+        else:
+            batch["target_text"] = batch[text_column_name].lower() + " "
+        return batch
+
+    with training_args.main_process_first(desc="dataset map special characters removal"):
+        raw_datasets = raw_datasets.map(
+            remove_special_characters,
+            remove_columns=[text_column_name],
+            desc="remove special characters from datasets",
+        )
+
+    # save special tokens for tokenizer
+    word_delimiter_token = data_args.word_delimiter_token
+    unk_token = data_args.unk_token
+    pad_token = data_args.pad_token
+
+    # 3. Next, let's load the config as we might need it to create
+    # the tokenizer
+    # load config
+    config = AutoConfig.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+    )
+
+    # 4. Next, if no tokenizer file is defined,
+    # we create the vocabulary of the model by extracting all unique characters from
+    # the training and evaluation datasets
+    # We need to make sure that only first rank saves vocabulary
+    # make sure all processes wait until vocab is created
+    tokenizer_name_or_path = model_args.tokenizer_name_or_path
+    tokenizer_kwargs = {}
+    if tokenizer_name_or_path is None:
+        # save vocab in training output dir
+        tokenizer_name_or_path = training_args.output_dir
+
+        vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
+
+        with training_args.main_process_first():
+            if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
+                try:
+                    os.remove(vocab_file)
+                except OSError:
+                    # in shared file-systems it might be the case that
+                    # two processes try to delete the vocab file at the some time
+                    pass
+
+        with training_args.main_process_first(desc="dataset map vocabulary creation"):
+            if not os.path.isfile(vocab_file):
+                os.makedirs(tokenizer_name_or_path, exist_ok=True)
+                vocab_dict = create_vocabulary_from_data(
+                    raw_datasets,
+                    word_delimiter_token=word_delimiter_token,
+                    unk_token=unk_token,
+                    pad_token=pad_token,
+                )
+
+                # save vocab dict to be loaded into tokenizer
+                with open(vocab_file, "w") as file:
+                    json.dump(vocab_dict, file)
+
+        # if tokenizer has just been created
+        # it is defined by `tokenizer_class` if present in config else by `model_type`
+        tokenizer_kwargs = {
+            "config": config if config.tokenizer_class is not None else None,
+            "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
+            "unk_token": unk_token,
+            "pad_token": pad_token,
+            "word_delimiter_token": word_delimiter_token,
+        }
+
+    # 5. Now we can instantiate the feature extractor, tokenizer and model
+    # Note for distributed training, the .from_pretrained methods guarantee that only
+    # one local process can concurrently download model & vocab.
+
+    # load feature_extractor and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer_name_or_path,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+        **tokenizer_kwargs,
+    )
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+    )
+
+    # adapt config
+    config.update(
+        {
+            "feat_proj_dropout": model_args.feat_proj_dropout,
+            "attention_dropout": model_args.attention_dropout,
+            "hidden_dropout": model_args.hidden_dropout,
+            "final_dropout": model_args.final_dropout,
+            "mask_time_prob": model_args.mask_time_prob,
+            "mask_time_length": model_args.mask_time_length,
+            "mask_feature_prob": model_args.mask_feature_prob,
+            "mask_feature_length": model_args.mask_feature_length,
+            "gradient_checkpointing": training_args.gradient_checkpointing,
+            "layerdrop": model_args.layerdrop,
+            "ctc_loss_reduction": model_args.ctc_loss_reduction,
+            "ctc_zero_infinity": model_args.ctc_zero_infinity,
+            "pad_token_id": tokenizer.pad_token_id,
+            "vocab_size": len(tokenizer),
+            "activation_dropout": model_args.activation_dropout,
+            "add_adapter": model_args.add_adapter,
+        }
+    )
+
+    # create model
+    model = AutoModelForCTC.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        config=config,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+    )
+
+    # freeze encoder
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.apply_trp:
+        model = apply_trp(model, model_args.trp_depths, model_args.trp_p, model_args.trp_lambdas)
+
+    # 6. Now we preprocess the datasets including loading the audio, resampling and normalization
+    # Thankfully, `datasets` takes care of automatically loading and resampling the audio,
+    # so that we just need to set the correct target sampling rate and normalize the input
+    # via the `feature_extractor`
+
+    # make sure that dataset decodes audio with correct sampling rate
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # derive max & min input length for sample rate & max duration
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    feature_extractor_input_name = feature_extractor.model_input_names[0]
+
+    # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
+    phoneme_language = data_args.phoneme_language
+
+    # Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    def prepare_dataset(batch):
+        # load audio
+        sample = batch[audio_column_name]
+
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        batch[feature_extractor_input_name] = getattr(inputs, feature_extractor_input_name)[0]
+        # take length of raw audio waveform
+        batch["input_length"] = len(sample["array"].squeeze())
+
+        # encode targets
+        additional_kwargs = {}
+        if phoneme_language is not None:
+            additional_kwargs["phonemizer_lang"] = phoneme_language
+
+        batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map preprocessing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=next(iter(raw_datasets.values())).column_names,
+            num_proc=num_workers,
+            desc="preprocess datasets",
+        )
+
+        def is_audio_in_length_range(length):
+            return length > min_input_length and length < max_input_length
+
+        # filter data that is shorter than min_input_length
+        vectorized_datasets = vectorized_datasets.filter(
+            is_audio_in_length_range,
+            num_proc=num_workers,
+            input_columns=["input_length"],
+        )
+
+    # 7. Next, we can prepare the training.
+    # Let's use word error rate (WER) as our evaluation metric,
+    # instantiate a data collator and the trainer
+
+    # Define evaluation metrics during training, *i.e.* word error rate, character error rate
+    eval_metrics = {metric: evaluate.load(metric, cache_dir=model_args.cache_dir) for metric in data_args.eval_metrics}
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with ``args.preprocessing_only`` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step ``args.preprocessing_only`` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
+        return
+
+    # For languages like Chinese with large vocabulary size, we need to discard logits
+    # and only keep the argmax, otherwise we run out of memory during evaluation.
+    def preprocess_logits_for_metrics(logits, labels):
+        pred_ids = torch.argmax(logits, dim=-1)
+        return pred_ids, labels
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions[0]
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
+
+        metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
+
+        return metrics
+
+    # Now save everything to be able to create a single processor later
+    # make sure all processes wait until data is saved
+    with training_args.main_process_first():
+        # only the main process saves them
+        if is_main_process(training_args.local_rank):
+            # save feature extractor, tokenizer and config
+            feature_extractor.save_pretrained(training_args.output_dir)
+            tokenizer.save_pretrained(training_args.output_dir)
+            config.save_pretrained(training_args.output_dir)
+
+    try:
+        processor = AutoProcessor.from_pretrained(training_args.output_dir)
+    except (OSError, KeyError):
+        warnings.warn(
+            "Loading a processor from a feature extractor config that does not"
+            " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
+            " attribute to your `preprocessor_config.json` file to suppress this warning: "
+            " `'processor_class': 'Wav2Vec2Processor'`",
+            FutureWarning,
+        )
+        processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
+
+    # Instantiate custom data collator
+    data_collator = DataCollatorCTCWithPadding(
+        processor=processor, feature_extractor_input_name=feature_extractor_input_name
+    )
+
+    # Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        data_collator=data_collator,
+        args=training_args,
+        compute_metrics=compute_metrics,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        processing_class=processor,
+        preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+    )
+
+    # 8. Finally, we can start training
+
+    # Training
+    if training_args.do_train:
+        # use last checkpoint if exist
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples
+            if data_args.max_train_samples is not None
+            else len(vectorized_datasets["train"])
+        )
+        metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+        max_eval_samples = (
+            data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
+        )
+        metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Write model card and (optionally) push to hub
+    config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": ["automatic-speech-recognition", data_args.dataset_name],
+        "dataset_args": (
+            f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:"
+            f" {data_args.eval_split_name}"
+        ),
+        "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
+    }
+    if "common_voice" in data_args.dataset_name:
+        kwargs["language"] = config_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hpo-examples/speech-recognition/run_speech_recognition_ctc_adapter.py b/hpo-examples/speech-recognition/run_speech_recognition_ctc_adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..062fddffb18eacacb3b148099081e8b6d27f3ad7
--- /dev/null
+++ b/hpo-examples/speech-recognition/run_speech_recognition_ctc_adapter.py
@@ -0,0 +1,823 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Fine-tuning a 🤗 Transformers CTC adapter model for automatic speech recognition"""
+
+import functools
+import json
+import logging
+import os
+import re
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import numpy as np
+import torch
+from datasets import DatasetDict, load_dataset
+from safetensors.torch import save_file as safe_save_file
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForCTC,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    Wav2Vec2Processor,
+    set_seed,
+)
+from transformers.models.wav2vec2.modeling_wav2vec2 import WAV2VEC2_ADAPTER_SAFE_FILE
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.50.0.dev0")
+
+require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+
+logger = logging.getLogger(__name__)
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    tokenizer_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    final_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout probability for the final projection layer."},
+    )
+    mask_time_prob: float = field(
+        default=0.05,
+        metadata={
+            "help": (
+                "Probability of each feature vector along the time axis to be chosen as the start of the vector "
+                "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature "
+                "vectors will be masked along the time axis."
+            )
+        },
+    )
+    mask_time_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the time axis."},
+    )
+    mask_feature_prob: float = field(
+        default=0.0,
+        metadata={
+            "help": (
+                "Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan"
+                " to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature"
+                " bins will be masked along the time axis."
+            )
+        },
+    )
+    mask_feature_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the feature axis."},
+    )
+    layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
+    ctc_loss_reduction: Optional[str] = field(
+        default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
+    )
+    adapter_attn_dim: int = field(
+        default=16,
+        metadata={
+            "help": "The hidden dimension of the adapter layers that will be randomly initialized and trained. The higher the dimension, the more capacity is given to the adapter weights. Note that only the adapter weights are fine-tuned."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: str = field(
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    target_language: Optional[str] = field(
+        metadata={
+            "help": (
+                "The target language on which the adapter attention layers"
+                " should be trained on in ISO 693-3 code, e.g. `tur` for Turkish"
+                " Wav2Vec2's MMS ISO codes can be looked up here: https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html"
+                " If you are not training the adapter layers on a language, simply choose"
+                " another acronym that fits your data."
+            )
+        },
+    )
+    dataset_config_name: str = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_split_name: str = field(
+        default="train+validation",
+        metadata={
+            "help": (
+                "The name of the training data set split to use (via the datasets library). Defaults to "
+                "'train+validation'"
+            )
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'test'"
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of validation examples to this "
+                "value if set."
+            )
+        },
+    )
+    chars_to_ignore: Optional[List[str]] = list_field(
+        default=None,
+        metadata={"help": "A list of characters to remove from the transcripts."},
+    )
+    eval_metrics: List[str] = list_field(
+        default=["wer"],
+        metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Filter audio files that are longer than `max_duration_in_seconds` seconds to"
+                " 'max_duration_in_seconds`"
+            )
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is especially useful when data"
+                " preprocessing errors out in distributed training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
+                " can consequently be loaded in distributed training"
+            )
+        },
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    unk_token: str = field(
+        default="[UNK]",
+        metadata={"help": "The unk token for the tokenizer"},
+    )
+    pad_token: str = field(
+        default="[PAD]",
+        metadata={"help": "The padding token for the tokenizer"},
+    )
+    word_delimiter_token: str = field(
+        default="|",
+        metadata={"help": "The word delimiter token for the tokenizer"},
+    )
+    overwrite_lang_vocab: bool = field(
+        default=False,
+        metadata={"help": ("If :obj:`True`, will overwrite existing `target_language` vocabulary of tokenizer.")},
+    )
+
+
+@dataclass
+class DataCollatorCTCWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor (:class:`~transformers.AutoProcessor`)
+            The processor used for proccessing the data.
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        max_length (:obj:`int`, `optional`):
+            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
+        max_length_labels (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` returned list and optionally padding length (see above).
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
+            7.5 (Volta).
+    """
+
+    processor: AutoProcessor
+    padding: Union[bool, str] = "longest"
+    pad_to_multiple_of: Optional[int] = None
+    pad_to_multiple_of_labels: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        input_features = [{"input_values": feature["input_values"]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.pad(
+            input_features,
+            padding=self.padding,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+        labels_batch = self.processor.pad(
+            labels=label_features,
+            padding=self.padding,
+            pad_to_multiple_of=self.pad_to_multiple_of_labels,
+            return_tensors="pt",
+        )
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        batch["labels"] = labels
+        if "attention_mask" in batch:
+            batch["attention_mask"] = batch["attention_mask"].to(torch.long)
+
+        return batch
+
+
+def create_vocabulary_from_data(
+    datasets: DatasetDict,
+    word_delimiter_token: Optional[str] = None,
+    unk_token: Optional[str] = None,
+    pad_token: Optional[str] = None,
+):
+    # Given training and test labels create vocabulary
+    def extract_all_chars(batch):
+        all_text = " ".join(batch["target_text"])
+        vocab = list(set(all_text))
+        return {"vocab": [vocab], "all_text": [all_text]}
+
+    vocabs = datasets.map(
+        extract_all_chars,
+        batched=True,
+        batch_size=-1,
+        keep_in_memory=True,
+        remove_columns=datasets["train"].column_names,
+    )
+
+    # take union of all unique characters in each dataset
+    vocab_set = functools.reduce(
+        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
+    )
+
+    vocab_dict = {v: k for k, v in enumerate(sorted(vocab_set))}
+
+    # replace white space with delimiter token
+    if word_delimiter_token is not None:
+        vocab_dict[word_delimiter_token] = vocab_dict[" "]
+        del vocab_dict[" "]
+
+    # add unk and pad token
+    if unk_token is not None:
+        vocab_dict[unk_token] = len(vocab_dict)
+
+    if pad_token is not None:
+        vocab_dict[pad_token] = len(vocab_dict)
+
+    return vocab_dict
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_speech_recognition_ctc_adapter", model_args, data_args)
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 1. First, let's load the dataset
+    raw_datasets = DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            token=data_args.token,
+            trust_remote_code=data_args.trust_remote_code,
+        )
+
+        if data_args.audio_column_name not in raw_datasets["train"].column_names:
+            raise ValueError(
+                f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'."
+                " Make sure to set `--audio_column_name` to the correct audio column - one of"
+                f" {', '.join(raw_datasets['train'].column_names)}."
+            )
+
+        if data_args.text_column_name not in raw_datasets["train"].column_names:
+            raise ValueError(
+                f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+                "Make sure to set `--text_column_name` to the correct text column - one of "
+                f"{', '.join(raw_datasets['train'].column_names)}."
+            )
+
+        if data_args.max_train_samples is not None:
+            raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            token=data_args.token,
+            trust_remote_code=data_args.trust_remote_code,
+        )
+
+        if data_args.max_eval_samples is not None:
+            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+
+    # 2. We remove some special characters from the datasets
+    # that make training complicated and do not help in transcribing the speech
+    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
+    # that could be easily picked up by the model
+    chars_to_ignore_regex = (
+        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
+    )
+    text_column_name = data_args.text_column_name
+
+    def remove_special_characters(batch):
+        if chars_to_ignore_regex is not None:
+            batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
+        else:
+            batch["target_text"] = batch[text_column_name].lower() + " "
+        return batch
+
+    with training_args.main_process_first(desc="dataset map special characters removal"):
+        raw_datasets = raw_datasets.map(
+            remove_special_characters,
+            remove_columns=[text_column_name],
+            desc="remove special characters from datasets",
+        )
+
+    # save special tokens for tokenizer
+    word_delimiter_token = data_args.word_delimiter_token
+    unk_token = data_args.unk_token
+    pad_token = data_args.pad_token
+
+    # 3. Next, let's load the config as we might need it to create
+    # the tokenizer
+    # load config
+    config = AutoConfig.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+    )
+
+    # 4. Next, if no tokenizer file is defined,
+    # we create the vocabulary of the model by extracting all unique characters from
+    # the training and evaluation datasets
+    # We need to make sure that only first rank saves vocabulary
+    # make sure all processes wait until vocab is created
+    tokenizer_name_or_path = model_args.tokenizer_name_or_path
+    tokenizer_kwargs = {}
+
+    vocab_dict = {}
+    if tokenizer_name_or_path is not None:
+        # load vocabulary of other adapter languages so that new language can be appended
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name_or_path,
+            token=data_args.token,
+            trust_remote_code=data_args.trust_remote_code,
+        )
+        vocab_dict = tokenizer.vocab.copy()
+        if tokenizer.target_lang is None:
+            raise ValueError("Make sure to load a multi-lingual tokenizer with a set target language.")
+
+        if data_args.target_language in tokenizer.vocab and not data_args.overwrite_lang_vocab:
+            logger.info(
+                "Adapter language already exists."
+                " Skipping vocabulary creating. If you want to create a new vocabulary"
+                f" for {data_args.target_language} make sure to add '--overwrite_lang_vocab'"
+            )
+        else:
+            tokenizer_name_or_path = None
+
+    if tokenizer_name_or_path is None:
+        # save vocab in training output dir
+        tokenizer_name_or_path = training_args.output_dir
+
+        vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
+
+        with training_args.main_process_first():
+            if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
+                try:
+                    os.remove(vocab_file)
+                except OSError:
+                    # in shared file-systems it might be the case that
+                    # two processes try to delete the vocab file at the some time
+                    pass
+
+        with training_args.main_process_first(desc="dataset map vocabulary creation"):
+            if not os.path.isfile(vocab_file):
+                os.makedirs(tokenizer_name_or_path, exist_ok=True)
+                lang_dict = create_vocabulary_from_data(
+                    raw_datasets,
+                    word_delimiter_token=word_delimiter_token,
+                    unk_token=unk_token,
+                    pad_token=pad_token,
+                )
+
+                # if we doing adapter language training, save
+                # vocab with adpter language
+                if data_args.target_language is not None:
+                    vocab_dict[data_args.target_language] = lang_dict
+
+                # save vocab dict to be loaded into tokenizer
+                with open(vocab_file, "w") as file:
+                    json.dump(vocab_dict, file)
+
+        # if tokenizer has just been created
+        # it is defined by `tokenizer_class` if present in config else by `model_type`
+        tokenizer_kwargs = {
+            "config": config if config.tokenizer_class is not None else None,
+            "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
+            "unk_token": unk_token,
+            "pad_token": pad_token,
+            "word_delimiter_token": word_delimiter_token,
+            "target_lang": data_args.target_language,
+        }
+
+    # 5. Now we can instantiate the feature extractor, tokenizer and model
+    # Note for distributed training, the .from_pretrained methods guarantee that only
+    # one local process can concurrently download model & vocab.
+
+    # load feature_extractor and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer_name_or_path,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+        **tokenizer_kwargs,
+    )
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+    )
+
+    # adapt config
+    config.update(
+        {
+            "final_dropout": model_args.final_dropout,
+            "mask_time_prob": model_args.mask_time_prob,
+            "mask_time_length": model_args.mask_time_length,
+            "mask_feature_prob": model_args.mask_feature_prob,
+            "mask_feature_length": model_args.mask_feature_length,
+            "gradient_checkpointing": training_args.gradient_checkpointing,
+            "layerdrop": model_args.layerdrop,
+            "ctc_loss_reduction": model_args.ctc_loss_reduction,
+            "pad_token_id": tokenizer.pad_token_id,
+            "vocab_size": len(tokenizer),
+            "adapter_attn_dim": model_args.adapter_attn_dim,
+        }
+    )
+
+    # create model
+    model = AutoModelForCTC.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        config=config,
+        token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
+        ignore_mismatched_sizes=True,
+    )
+
+    # if attn adapter is defined, freeze all non-adapter weights
+    if model.config.adapter_attn_dim is not None:
+        model.init_adapter_layers()
+        # first we freeze the whole base model
+        model.freeze_base_model()
+
+        # next we unfreeze all adapter layers
+        adapter_weights = model._get_adapters()
+        for param in adapter_weights.values():
+            param.requires_grad = True
+
+    # 6. Now we preprocess the datasets including loading the audio, resampling and normalization
+    # Thankfully, `datasets` takes care of automatically loading and resampling the audio,
+    # so that we just need to set the correct target sampling rate and normalize the input
+    # via the `feature_extractor`
+
+    # make sure that dataset decodes audio with correct sampling rate
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # derive max & min input length for sample rate & max duration
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+
+    # Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    def prepare_dataset(batch):
+        # load audio
+        sample = batch[audio_column_name]
+
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        batch["input_values"] = inputs.input_values[0]
+        batch["input_length"] = len(batch["input_values"])
+
+        # encode targets
+        batch["labels"] = tokenizer(batch["target_text"]).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map preprocessing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=next(iter(raw_datasets.values())).column_names,
+            num_proc=num_workers,
+            desc="preprocess datasets",
+        )
+
+        def is_audio_in_length_range(length):
+            return length > min_input_length and length < max_input_length
+
+        # filter data that is shorter than min_input_length
+        vectorized_datasets = vectorized_datasets.filter(
+            is_audio_in_length_range,
+            num_proc=num_workers,
+            input_columns=["input_length"],
+        )
+
+    # 7. Next, we can prepare the training.
+    # Let's use word error rate (WER) as our evaluation metric,
+    # instantiate a data collator and the trainer
+
+    # Define evaluation metrics during training, *i.e.* word error rate, character error rate
+    eval_metrics = {metric: evaluate.load(metric, cache_dir=model_args.cache_dir) for metric in data_args.eval_metrics}
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with ``args.preprocessing_only`` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step ``args.preprocessing_only`` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
+        return
+
+    def compute_metrics(pred):
+        pred_logits = pred.predictions
+        pred_ids = np.argmax(pred_logits, axis=-1)
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
+
+        metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
+
+        return metrics
+
+    # Now save everything to be able to create a single processor later
+    # make sure all processes wait until data is saved
+    with training_args.main_process_first():
+        # only the main process saves them
+        if is_main_process(training_args.local_rank):
+            # save feature extractor, tokenizer and config
+            feature_extractor.save_pretrained(training_args.output_dir)
+            tokenizer.save_pretrained(training_args.output_dir)
+            config.save_pretrained(training_args.output_dir)
+
+    try:
+        processor = AutoProcessor.from_pretrained(training_args.output_dir)
+    except (OSError, KeyError):
+        warnings.warn(
+            "Loading a processor from a feature extractor config that does not"
+            " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
+            " attribute to your `preprocessor_config.json` file to suppress this warning: "
+            " `'processor_class': 'Wav2Vec2Processor'`",
+            FutureWarning,
+        )
+        processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
+
+    # Instantiate custom data collator
+    data_collator = DataCollatorCTCWithPadding(processor=processor)
+
+    # Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        data_collator=data_collator,
+        args=training_args,
+        compute_metrics=compute_metrics,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        processing_class=processor,
+    )
+
+    # 8. Finally, we can start training
+
+    # Training
+    if training_args.do_train:
+        # use last checkpoint if exist
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples
+            if data_args.max_train_samples is not None
+            else len(vectorized_datasets["train"])
+        )
+        metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+        max_eval_samples = (
+            data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
+        )
+        metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Write model card and (optionally) push to hub
+    config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": ["automatic-speech-recognition", data_args.dataset_name, "mms"],
+        "dataset_args": (
+            f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:"
+            f" {data_args.eval_split_name}"
+        ),
+        "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
+    }
+    if "common_voice" in data_args.dataset_name:
+        kwargs["language"] = config_name
+
+    # make sure that adapter weights are saved seperately
+    adapter_file = WAV2VEC2_ADAPTER_SAFE_FILE.format(data_args.target_language)
+    adapter_file = os.path.join(training_args.output_dir, adapter_file)
+    logger.info(f"Saving adapter weights under {adapter_file}...")
+    safe_save_file(model._get_adapters(), adapter_file, metadata={"format": "pt"})
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hpo-examples/speech-recognition/run_speech_recognition_seq2seq.py b/hpo-examples/speech-recognition/run_speech_recognition_seq2seq.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d222ff61a3fedd1e9a3651cc6eaa24cdc5047c5
--- /dev/null
+++ b/hpo-examples/speech-recognition/run_speech_recognition_seq2seq.py
@@ -0,0 +1,634 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for sequence to sequence speech recognition.
+"""
+# You can also adapt this script on your own sequence to sequence speech
+# recognition task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import torch
+from datasets import DatasetDict, load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.50.0.dev0")
+
+require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    freeze_encoder: bool = field(
+        default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
+    )
+    forced_decoder_ids: List[List[int]] = field(
+        default=None,
+        metadata={"help": "Deprecated. Please use the `language` and `task` arguments instead."},
+    )
+    suppress_tokens: List[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Deprecated. The use of `suppress_tokens` should not be required for the majority of fine-tuning examples."
+                "Should you need to use `suppress_tokens`, please manually update them in the fine-tuning script directly."
+            )
+        },
+    )
+    apply_spec_augment: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to apply *SpecAugment* data augmentation to the input features. This is currently only relevant for Wav2Vec2, HuBERT, WavLM and Whisper models."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
+                " 'max_duration_in_seconds`"
+            )
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is especially useful when data"
+                " preprocessing errors out in distributed training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
+                " can consequently be loaded in distributed training"
+            )
+        },
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    do_lower_case: bool = field(
+        default=True,
+        metadata={"help": "Whether the target text should be lower cased."},
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
+                "only. For English speech recognition, it should be set to `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`WhisperProcessor`])
+            The processor used for processing the data.
+        decoder_start_token_id (`int`)
+            The begin-of-sentence of the decoder.
+        forward_attention_mask (`bool`)
+            Whether to return attention_mask.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    forward_attention_mask: bool
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+        input_features = [{model_input_name: feature[model_input_name]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
+
+        if self.forward_attention_mask:
+            batch["attention_mask"] = torch.LongTensor([feature["attention_mask"] for feature in features])
+
+        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args)
+
+    # 2. Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Detecting last checkpoint and eventually continue from last checkpoint
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 4. Load dataset
+    raw_datasets = DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
+        )
+
+    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+
+    # SpecAugment for whisper models
+    if getattr(config, "model_type", None) == "whisper":
+        config.update({"apply_spec_augment": model_args.apply_spec_augment})
+
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+        model.model.encoder.gradient_checkpointing = False
+
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        # We only need to set the language and task ids in a multilingual setting
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+        model.generation_config.language = data_args.language
+        model.generation_config.task = data_args.task
+    elif data_args.language is not None:
+        raise ValueError(
+            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
+            "only be set for multilingual checkpoints."
+        )
+
+    # TODO (Sanchit): deprecate these arguments in v4.41
+    if model_args.forced_decoder_ids is not None:
+        logger.warning(
+            "The use of `forced_decoder_ids` is deprecated and will be removed in v4.41."
+            "Please use the `language` and `task` arguments instead"
+        )
+        model.generation_config.forced_decoder_ids = model_args.forced_decoder_ids
+    else:
+        model.generation_config.forced_decoder_ids = None
+        model.config.forced_decoder_ids = None
+
+    if model_args.suppress_tokens is not None:
+        logger.warning(
+            "The use of `suppress_tokens` is deprecated and will be removed in v4.41."
+            "Should you need `suppress_tokens`, please manually set them in the fine-tuning script."
+        )
+        model.generation_config.suppress_tokens = model_args.suppress_tokens
+
+    # 6. Resample speech dataset if necessary
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    do_lower_case = data_args.do_lower_case
+    # if SpecAugment is used for whisper models, return attention_mask to guide the mask along time axis
+    forward_attention_mask = (
+        getattr(config, "model_type", None) == "whisper"
+        and getattr(config, "apply_spec_augment", False)
+        and getattr(config, "mask_time_prob", 0) > 0
+    )
+
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(
+            sample["array"], sampling_rate=sample["sampling_rate"], return_attention_mask=forward_attention_mask
+        )
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+        if forward_attention_mask:
+            batch["attention_mask"] = inputs.get("attention_mask")[0]
+
+        # process targets
+        input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=next(iter(raw_datasets.values())).column_names,
+            num_proc=data_args.preprocessing_num_workers,
+            desc="preprocess train dataset",
+        )
+
+    # filter data that is shorter than min_input_length or longer than
+    # max_input_length
+    def is_audio_in_length_range(length):
+        return length > min_input_length and length < max_input_length
+
+    vectorized_datasets = vectorized_datasets.filter(
+        is_audio_in_length_range,
+        num_proc=num_workers,
+        input_columns=["input_length"],
+    )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 8. Load Metric
+    metric = evaluate.load("wer", cache_dir=model_args.cache_dir)
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
+
+        wer = metric.compute(predictions=pred_str, references=label_str)
+
+        return {"wer": wer}
+
+    # 9. Create a single speech processor
+    # make sure all processes wait until data is saved
+    with training_args.main_process_first():
+        # only the main process saves them
+        if is_main_process(training_args.local_rank):
+            # save feature extractor, tokenizer and config
+            feature_extractor.save_pretrained(training_args.output_dir)
+            tokenizer.save_pretrained(training_args.output_dir)
+            config.save_pretrained(training_args.output_dir)
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    # 10. Define data collator
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+        forward_attention_mask=forward_attention_mask,
+    )
+
+    # 11. Initialize Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        processing_class=feature_extractor,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+    )
+
+    # 12. Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the feature extractor too for easy upload
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples
+            if data_args.max_train_samples is not None
+            else len(vectorized_datasets["train"])
+        )
+        metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
+        )
+        max_eval_samples = (
+            data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
+        )
+        metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 14. Write Training Stats
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hpo-examples/speech-recognition/trplib.py b/hpo-examples/speech-recognition/trplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0fb33879b1ab05f53e0b8e7068729abbb96146
--- /dev/null
+++ b/hpo-examples/speech-recognition/trplib.py
@@ -0,0 +1,1181 @@
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from torchvision.models.mobilenetv2 import MobileNetV2
+from torchvision.models.resnet import ResNet
+from torchvision.models.efficientnet import EfficientNet
+from torchvision.models.vision_transformer import VisionTransformer
+from torchvision.models.segmentation.fcn import FCN
+from torchvision.models.segmentation.deeplabv3 import DeepLabV3
+
+import transformers
+from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, CausalLMOutput, Seq2SeqLMOutput
+
+from typing import Optional, Tuple, List, Union, Callable
+from collections import OrderedDict
+import types
+
+
+def trp_criterion(trp_blocks: nn.ModuleList, shared_head: Callable, criterion: Callable, lambdas: List[float], hidden_states: Tensor, logits: Tensor, targets: Tensor, loss_normalization=False):
+    loss, mask = criterion(logits, targets)
+    if loss_normalization:
+        coeff = loss.detach()
+
+    embeds = [hidden_states]
+    predictions = []
+    for k, c in enumerate(lambdas):
+        embeds.append(trp_blocks[k](embeds[-1]))
+        predictions.append(shared_head(embeds[-1]))
+        replica_loss, mask = criterion(predictions[-1], targets, mask)
+        loss += c * replica_loss
+    
+    if loss_normalization:
+        with torch.no_grad():
+            coeff = torch.exp(coeff) / torch.exp(loss.detach())
+        loss = coeff * loss
+    
+    return loss
+
+
+class TPBlock(nn.Module):
+    def __init__(self, depths: int, in_features: int, p: float, dim=-1):
+        super(TPBlock, self).__init__()
+
+        self.dropout = nn.Dropout(p)
+
+        self.cdim = dim
+
+        blocks = []
+        for _ in range(depths):
+            blocks.append(nn.Linear(in_features, in_features))
+            nn.init.constant_(blocks[-1].weight, 0.0)
+            nn.init.constant_(blocks[-1].bias, 0.0)
+            blocks.append(nn.ReLU())
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        x = self.dropout(x)
+        if self.cdim == -1:
+            x = x + self.blocks(x)
+        else:
+            x = x + torch.movedim(self.blocks(torch.movedim(x, self.cdim, -1)), -1, self.cdim)
+        return x
+    
+
+class Config:
+    @staticmethod
+    def gen_criterion(*args, **kwargs):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor.
+                target (Tensor): Target labels.
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor with the same shape of target.
+            """
+            pass
+        return func
+    
+    @staticmethod
+    def gen_shared_head(*args, **kwargs):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States tensor.
+
+            Returns:
+                logits (Tensor): Logits tensor.
+            """
+            pass
+        return func
+
+    @staticmethod
+    def forward(*args, **kwargs):
+        pass
+
+
+# Wav2Vec2 for Audio Classification
+class Wav2Vec2ForSequenceClassificationConfig(Config): 
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self, attention_mask):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+            output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            if self.config.use_weighted_layer_sum:
+                hidden_states = outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION]
+                hidden_states = torch.stack(hidden_states, dim=1)
+                norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+                hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+            else:
+                hidden_states = outputs[0]
+
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                shared_head = Wav2Vec2ForSequenceClassificationConfig.gen_shared_head(self, attention_mask)
+                criterion = Wav2Vec2ForSequenceClassificationConfig.gen_criterion()
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states, logits.view(-1, self.config.num_labels),  labels.view(-1), loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# MobileNetV2 for Image Classification
+class MobileNetV2Config(Config):
+    @staticmethod
+    def gen_criterion(label_smoothing=0.0, top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B] or [B, C].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            label = torch.argmax(target, dim=1) if label_smoothing > 0.0 else target
+                
+            unmasked_loss = F.cross_entropy(input, label, reduction="none", label_smoothing=label_smoothing)
+            if mask is None:
+                mask = torch.ones_like(unmasked_loss, dtype=torch.float32, device=target.device)
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=-1)
+                mask = mask * torch.eq(topk_indices, label[:, None]).any(dim=-1).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = MobileNetV2Config.gen_shared_head(self)
+                criterion = MobileNetV2Config.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ResNet for Image Classification
+class ResNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.fc(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.conv1(images)
+            x = self.bn1(x)
+            x = self.relu(x)
+            x = self.maxpool(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.fc(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = ResNetConfig.gen_shared_head(self)
+                criterion = ResNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# EfficientNet for Image Classification
+class EfficientNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = EfficientNetConfig.gen_shared_head(self)
+                criterion = EfficientNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ViT for Image Classification
+class VisionTransformerConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.heads(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self._process_input(images)
+            n = x.shape[0]
+            batch_class_token = self.class_token.expand(n, -1, -1)
+            x = torch.cat([batch_class_token, x], dim=1)
+            x = self.encoder(x)
+            x = x[:, 0]
+
+            logits = self.heads(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = VisionTransformerConfig.gen_shared_head(self)
+                criterion = VisionTransformerConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# Bert for Question Answering
+class BertForQuestionAnsweringConfig(Config): 
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target: List[Tensor], mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C, 2].
+                target (List[Tensor]): 
+                    Start Positions of shape [B].
+                    End Positions of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            start_positions, end_positions = target
+
+            if mask is None:
+                mask = torch.ones_like(start_positions, dtype=torch.float32, device=start_positions.device)
+
+            start_logits, end_logits = input.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            masked_start_losses = F.cross_entropy(start_logits, start_positions, ignore_index=ignored_index, reduction="none")
+            start_loss = torch.sum(mask * masked_start_losses) / (torch.sum(mask) + 1e-6)
+            masked_end_losses = F.cross_entropy(end_logits, end_positions, ignore_index=ignored_index, reduction="none")
+            end_loss = torch.sum(mask * masked_end_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(start_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, start_positions[:, None]).any(dim=1).to(start_logits.dtype)
+                topk_values, topk_indices = torch.topk(end_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, end_positions[:, None]).any(dim=1).to(end_logits.dtype)
+
+            return (start_loss + end_loss) / 2, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.qa_outputs(hidden_states)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            start_positions: Optional[torch.Tensor] = None,
+            end_positions: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+            r"""
+            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the start of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the end of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            sequence_output = outputs[0]
+
+            logits = self.qa_outputs(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                shared_head = BertForQuestionAnsweringConfig.gen_shared_head(self)
+                criterion = BertForQuestionAnsweringConfig.gen_criterion()
+                total_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, [start_positions, end_positions], loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+    
+
+# FCN for Semantic Segmentation
+class FCNConfig(Config):
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input Tensor: input tensor of shape [B, C, H, W].
+                target (Tensor): Target labels of shape [B, H, W].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B, H, W].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+                
+            masked_loss = F.cross_entropy(input, target, ignore_index=255, reduction="none")
+            loss = torch.sum(mask * masked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, target[:, None, :, :]).any(dim=1).to(input.dtype)
+                # mask = mask * torch.eq(torch.argmax(x, dim=1), target).to(x.dtype)
+                
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_out_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+    
+    @staticmethod
+    def gen_aux_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.aux_classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            input_shape = images.shape[-2:]
+            # contract: features is a dict of tensors
+            features = self.backbone(images)
+
+            result = OrderedDict()
+            x = features["out"]
+            x = self.classifier(x)
+            x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            result["out"] = x
+
+            if self.aux_classifier is not None:
+                x = features["aux"]
+                x = self.aux_classifier(x)
+                x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+                result["aux"] = x
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                out_shared_head = FCNConfig.gen_out_shared_head(self, input_shape)
+                aux_shared_head = FCNConfig.gen_aux_shared_head(self, input_shape)
+                criterion = FCNConfig.gen_criterion(top_k)
+                out_loss = trp_criterion(self.out_trp_blocks, out_shared_head, criterion, lambdas, features["out"], result["out"], targets, loss_normalization)
+                aux_loss = trp_criterion(self.aux_trp_blocks, aux_shared_head, criterion, lambdas, features["aux"], result["aux"], targets, loss_normalization)
+                loss = out_loss + 0.5 * aux_loss
+                return result, loss
+            return result
+        return func
+
+
+# DeepLabV3Config for Semantic Segmentation
+class DeepLabV3Config(FCNConfig):
+    pass
+
+
+# Bert for Text Classification
+class BertForSequenceClassificationConfig(Config): 
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, pooled_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Boberta for Text Classification
+class RobertaForSequenceClassificationConfig(BertForSequenceClassificationConfig): 
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.roberta(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            sequence_output = outputs[0]
+            logits = self.classifier(sequence_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                # move labels to correct device to enable model parallelism
+                labels = labels.to(logits.device)
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Wav2Vec2 for Speech Recognition
+class Wav2Vec2ForCTCConfig(Config):
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def greedy_decode_ctc(
+        log_probs: torch.Tensor,
+        input_lengths: torch.Tensor,
+        blank_token_id: int,
+        target_lengths: torch.Tensor
+    ):
+        """
+        Convert logits to flattened predictions that match the shape of flattened_targets.
+
+        Args:
+            log_probs: [B, L, V] - log-softmax output
+            input_lengths: [B] - actual length of each input
+            blank_token_id: int - index of blank token
+            target_lengths: [B] - used to determine how many predictions to keep per sample
+
+        Returns:
+            flattened_predictions: 1D tensor, same total length as sum(target_lengths)
+        """
+        batch_size = log_probs.size(0)
+        decoded_all = []
+
+        predicted_ids = log_probs.argmax(dim=-1)  # [B, L]
+
+        for i in range(batch_size):
+            pred = predicted_ids[i][:input_lengths[i]]  # [Li]
+            prev = None
+            decoded = []
+            for token in pred:
+                token = token.item()
+                if token != blank_token_id and token != prev:
+                    decoded.append(token)
+                prev = token
+            # Trim or pad to match target_lengths[i]
+            tgt_len = target_lengths[i].item()
+            if len(decoded) >= tgt_len:
+                decoded = decoded[:tgt_len]
+            else:
+                decoded = decoded + [blank_token_id] * (tgt_len - len(decoded))  # pad with blank
+            decoded_all.extend(decoded)
+
+        return torch.tensor(decoded_all, dtype=torch.long, device=log_probs.device)  # shape: [sum(target_lengths)]
+
+    @staticmethod
+    def gen_criterion(input_lengths: Tensor, pad_token_id: int, ctc_zero_infinity: bool):
+        def func(logits: Tensor, labels: Tensor, mask=None):
+            """
+            Args:
+                logits (Tensor): Log Probablities of shape [B, L, V].
+                labels (Tensor): Flattened Targets of shape [B, L'].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(input_lengths, dtype=torch.float32, device=input_lengths.device)
+
+            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            with torch.backends.cudnn.flags(enabled=False):
+                masked_losses = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=pad_token_id, reduction="none", zero_infinity=ctc_zero_infinity)
+                loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                thres = 0.5
+                flattened_predictions = Wav2Vec2ForCTCConfig.greedy_decode_ctc(
+                    log_probs.transpose(0, 1),  # [B, T, V]
+                    input_lengths=input_lengths,
+                    blank_token_id=pad_token_id,
+                    target_lengths=target_lengths
+                )
+                token_wise_mask =  torch.eq(flattened_predictions, flattened_targets).to(flattened_targets.dtype)
+                segment_ids = torch.arange(len(target_lengths), device=target_lengths.device).repeat_interleave(target_lengths)
+                sequence_wise_mask = torch.zeros(len(target_lengths), dtype=target_lengths.dtype, device=token_wise_mask.device).scatter_add(0, segment_ids, token_wise_mask)
+                mask = mask * torch.ge(sequence_wise_mask, thres * target_lengths).to(flattened_targets.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.lm_head(hidden_states)
+            # log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, CausalLMOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
+                Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
+                the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
+                All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
+                config.vocab_size - 1]`.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None and labels.max() >= self.config.vocab_size:
+                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            hidden_states = self.dropout(hidden_states)
+
+            logits = self.lm_head(hidden_states)
+
+            loss = None
+            if labels is not None:
+                # retrieve loss input_lengths from attention_mask
+                attention_mask = (
+                    attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
+                )
+                input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+                shared_head = Wav2Vec2ForCTCConfig.gen_shared_head(self)
+                criterion = Wav2Vec2ForCTCConfig.gen_criterion(input_lengths, self.config.pad_token_id, self.config.ctc_zero_infinity)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states,  logits, labels, loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForCTCConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return CausalLMOutput(
+                loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+            )
+        return func
+
+
+# MBart for Translation
+class MBartForConditionalGenerationConfig(Config): 
+    @staticmethod
+    def gen_criterion(vocab_size: int, top_k=1):
+        def func(logits, labels, mask=None):
+            """
+            Args:
+                logits (Tensor): Logits tensor of shape [B, L, V].
+                labels (Tensor): Target labels of shape [B, L].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(labels.view(-1), dtype=torch.float32, device=labels.device)
+                
+            masked_losses = F.cross_entropy(logits.view(-1, vocab_size), labels.view(-1), reduction="none")
+            loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(logits.view(-1, vocab_size), top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, labels.view(-1, 1)).any(dim=1).to(logits.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, L].
+            """
+            logits = self.lm_head(hidden_states) + self.final_logits_bias
+            return logits
+        return func
+    
+    @staticmethod
+    def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+        """
+        Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
+        have a single `decoder_start_token_id` in contrast to other Bart-like models.
+        """
+        prev_output_tokens = input_ids.clone()
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+        index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+        decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+        prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+        prev_output_tokens[:, 0] = decoder_start_tokens
+
+        return prev_output_tokens
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            Returns:
+
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None:
+                # if use_cache:
+                #     logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+                use_cache = False
+                if decoder_input_ids is None and decoder_inputs_embeds is None:
+                    decoder_input_ids = MBartForConditionalGenerationConfig.shift_tokens_right(labels, self.config.pad_token_id)
+
+            outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=decoder_input_ids,
+                encoder_outputs=encoder_outputs,
+                decoder_attention_mask=decoder_attention_mask,
+                head_mask=head_mask,
+                decoder_head_mask=decoder_head_mask,
+                cross_attn_head_mask=cross_attn_head_mask,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+            masked_lm_loss = None
+            if labels is not None:
+                shared_head = MBartForConditionalGenerationConfig.gen_shared_head(self)
+                criterion = MBartForConditionalGenerationConfig.gen_criterion(self.config.vocab_size)
+                masked_lm_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, outputs[0],  lm_logits, labels, loss_normalization)
+
+            if not return_dict:
+                output = (lm_logits,) + outputs[1:]
+                return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+            return Seq2SeqLMOutput(
+                loss=masked_lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                decoder_hidden_states=outputs.decoder_hidden_states,
+                decoder_attentions=outputs.decoder_attentions,
+                cross_attentions=outputs.cross_attentions,
+                encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+                encoder_hidden_states=outputs.encoder_hidden_states,
+                encoder_attentions=outputs.encoder_attentions,
+            )
+        return func
+
+
+def apply_trp(model, depths: int, p: float, lambdas: List[float], **kwargs):
+    if isinstance(model, transformers.Wav2Vec2ForSequenceClassification):
+        print("✅ Applying TRP to Wav2Vec2 for Audio Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, MobileNetV2):
+        print("✅ Applying TRP to MobileNetV2 for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(MobileNetV2Config.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, ResNet):
+        print("✅ Applying TRP to ResNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p) for _ in lambdas])
+        model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, EfficientNet):
+        print("✅ Applying TRP to EfficientNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(EfficientNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, VisionTransformer):
+        print("✅ Applying TRP to VisionTransformer for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(VisionTransformerConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, transformers.BertForQuestionAnswering):
+        print("✅ Applying TRP to Bert for Question Answering...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForQuestionAnsweringConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, FCN):
+        print("✅ Applying TRP to FCN for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(FCNConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, DeepLabV3):
+        print("✅ Applying TRP to DeepLabV3 for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(DeepLabV3Config.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, transformers.BertForSequenceClassification):
+        print("✅ Applying TRP to Bert for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.RobertaForSequenceClassification):
+        print("✅ Applying TRP to Roberta for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(RobertaForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.Wav2Vec2ForCTC):
+        print("✅ Applying TRP to Wav2Vec2 for Speech Recognition...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForCTCConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.MBartForConditionalGeneration):
+        print("✅ Applying TRP to MBart for Translation...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(MBartForConditionalGenerationConfig.gen_forward(lambdas, False), model)
+    else:
+        torch._assert(
+            isinstance(model, transformers.Wav2Vec2ForSequenceClassification), 
+            "The model should be an object of [`Wav2Vec2ForSequenceClassification`].")
+
+    return model
\ No newline at end of file
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diff --git a/hpo-examples/text-classification/__pycache__/trplib.cpython-39.pyc b/hpo-examples/text-classification/__pycache__/trplib.cpython-39.pyc
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diff --git a/hpo-examples/text-classification/cola/README.md b/hpo-examples/text-classification/cola/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..342b7eca0508b0b1313bd2b5fc50eb81f9279ffa
--- /dev/null
+++ b/hpo-examples/text-classification/cola/README.md
@@ -0,0 +1,73 @@
+---
+library_name: transformers
+language:
+- en
+license: apache-2.0
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- matthews_correlation
+model-index:
+- name: cola
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE COLA
+      type: glue
+      args: cola
+    metrics:
+    - name: Matthews Correlation
+      type: matthews_correlation
+      value: 0.6212584232587556
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# cola
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the GLUE COLA dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8440
+- Matthews Correlation: 0.6213
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2.5e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 6.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/text-classification/cola/all_results.json b/hpo-examples/text-classification/cola/all_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..0752f82188774641f8d97c8ba1d8f41c402862d6
--- /dev/null
+++ b/hpo-examples/text-classification/cola/all_results.json
@@ -0,0 +1,15 @@
+{
+    "epoch": 6.0,
+    "eval_loss": 0.8439584374427795,
+    "eval_matthews_correlation": 0.6212584232587556,
+    "eval_runtime": 2.0452,
+    "eval_samples": 1043,
+    "eval_samples_per_second": 509.983,
+    "eval_steps_per_second": 64.053,
+    "total_flos": 3444607255477248.0,
+    "train_loss": 0.2595453208951808,
+    "train_runtime": 282.1547,
+    "train_samples": 8551,
+    "train_samples_per_second": 181.836,
+    "train_steps_per_second": 5.699
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/cola/config.json b/hpo-examples/text-classification/cola/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..43ca98ba2ace1e7acbc2ea563555adf95805bf6d
--- /dev/null
+++ b/hpo-examples/text-classification/cola/config.json
@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "google-bert/bert-base-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "cola",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "acceptable": 1,
+    "unacceptable": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}
diff --git a/hpo-examples/text-classification/cola/eval_results.json b/hpo-examples/text-classification/cola/eval_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..7a4ccf80dad92f85753883328ea6ad87da7bf4b7
--- /dev/null
+++ b/hpo-examples/text-classification/cola/eval_results.json
@@ -0,0 +1,9 @@
+{
+    "epoch": 6.0,
+    "eval_loss": 0.8439584374427795,
+    "eval_matthews_correlation": 0.6212584232587556,
+    "eval_runtime": 2.0452,
+    "eval_samples": 1043,
+    "eval_samples_per_second": 509.983,
+    "eval_steps_per_second": 64.053
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/cola/model.safetensors b/hpo-examples/text-classification/cola/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..534c2c7fc94a9500ff5655b94b080f11580fb6a3
--- /dev/null
+++ b/hpo-examples/text-classification/cola/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3914a1bf70fbecb9956a8d59981be21e5576bb355f0ab579504e817dd59f90c7
+size 440358464
diff --git a/hpo-examples/text-classification/cola/runs/Jun11_13-41-15_cs-Precision-7960-Tower/events.out.tfevents.1749663679.cs-Precision-7960-Tower.10988.0 b/hpo-examples/text-classification/cola/runs/Jun11_13-41-15_cs-Precision-7960-Tower/events.out.tfevents.1749663679.cs-Precision-7960-Tower.10988.0
new file mode 100644
index 0000000000000000000000000000000000000000..03b4687fbacfcd7e66c8e2bf006bcb8594c055f7
--- /dev/null
+++ b/hpo-examples/text-classification/cola/runs/Jun11_13-41-15_cs-Precision-7960-Tower/events.out.tfevents.1749663679.cs-Precision-7960-Tower.10988.0
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+      "Table": 11389,
+      "UTC": 11390,
+      "Marina": 11391,
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+      "##enberg": 11588,
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+      "Dubai": 11593,
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+      "##bers": 11697,
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+      "Kazakhstan": 11816,
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+      "Guatemala": 11835,
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+      "sin": 11850,
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+      "Downtown": 11852,
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+      "Euro": 11854,
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+      "##ote": 11860,
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+      "##rrell": 11862,
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+      "colleague": 11864,
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+      "##makers": 11877,
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+      "illustrations": 11880,
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+      "Joyce": 11882,
+      "beats": 11883,
+      "editors": 11884,
+      "mount": 11885,
+      "##grave": 11886,
+      "Malaysian": 11887,
+      "Bombay": 11888,
+      "endorsed": 11889,
+      "##sian": 11890,
+      "##bee": 11891,
+      "applying": 11892,
+      "Religion": 11893,
+      "nautical": 11894,
+      "bomber": 11895,
+      "Na": 11896,
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+      "##rew": 11899,
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+      "bye": 11901,
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+      "Election": 11905,
+      "Hawk": 11906,
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+      "##iled": 11908,
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+      "##tland": 11910,
+      "liver": 11911,
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+      "Put": 11913,
+      "delegates": 11914,
+      "Ella": 11915,
+      "##fect": 11916,
+      "Cal": 11917,
+      "invention": 11918,
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+      "bored": 11920,
+      "##kawa": 11921,
+      "##ail": 11922,
+      "Trinidad": 11923,
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+      "##pton": 11927,
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+      "beta": 11933,
+      "parameters": 11934,
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+      "##lene": 11936,
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+      "Beat": 11938,
+      "blades": 11939,
+      "Either": 11940,
+      "##hale": 11941,
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+      "##ET": 11943,
+      "##vous": 11944,
+      "Rod": 11945,
+      "burnt": 11946,
+      "phones": 11947,
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+      "##front": 11949,
+      "investigating": 11950,
+      "##dent": 11951,
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+      "Nigerian": 11960,
+      "melody": 11961,
+      "##ques": 11962,
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+      "##12": 11964,
+      "128": 11965,
+      "##jin": 11966,
+      "charm": 11967,
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+      "##tian": 11969,
+      "fusion": 11970,
+      "advocated": 11971,
+      "visitor": 11972,
+      "pinned": 11973,
+      "genera": 11974,
+      "3000": 11975,
+      "Ferry": 11976,
+      "Solo": 11977,
+      "quantity": 11978,
+      "regained": 11979,
+      "platinum": 11980,
+      "shoots": 11981,
+      "narrowly": 11982,
+      "preceded": 11983,
+      "update": 11984,
+      "##ichi": 11985,
+      "equality": 11986,
+      "unaware": 11987,
+      "regiments": 11988,
+      "ally": 11989,
+      "##tos": 11990,
+      "transmitter": 11991,
+      "locks": 11992,
+      "Seeing": 11993,
+      "outlets": 11994,
+      "feast": 11995,
+      "reopened": 11996,
+      "##ows": 11997,
+      "struggles": 11998,
+      "Buddy": 11999,
+      "1826": 12000,
+      "bark": 12001,
+      "elegant": 12002,
+      "amused": 12003,
+      "Pretty": 12004,
+      "themed": 12005,
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new file mode 100644
index 0000000000000000000000000000000000000000..bd5a70f5066ffa2be3f1aa44710a783a881be878
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\ No newline at end of file
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diff --git a/hpo-examples/text-classification/mrpc/README.md b/hpo-examples/text-classification/mrpc/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..767118c81f676f6c05d53739a1a79f82a9a19678
--- /dev/null
+++ b/hpo-examples/text-classification/mrpc/README.md
@@ -0,0 +1,79 @@
+---
+library_name: transformers
+language:
+- en
+license: apache-2.0
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+- f1
+model-index:
+- name: mrpc
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE MRPC
+      type: glue
+      args: mrpc
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8725490196078431
+    - name: F1
+      type: f1
+      value: 0.910958904109589
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# mrpc
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the GLUE MRPC dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5399
+- Accuracy: 0.8725
+- F1: 0.9110
+- Combined Score: 0.8918
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 4.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/text-classification/mrpc/all_results.json b/hpo-examples/text-classification/mrpc/all_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..86d32e4161e020a15f1c38817600791e8b6faee6
--- /dev/null
+++ b/hpo-examples/text-classification/mrpc/all_results.json
@@ -0,0 +1,17 @@
+{
+    "epoch": 4.0,
+    "eval_accuracy": 0.8725490196078431,
+    "eval_combined_score": 0.891753961858716,
+    "eval_f1": 0.910958904109589,
+    "eval_loss": 0.5399143695831299,
+    "eval_runtime": 0.8251,
+    "eval_samples": 408,
+    "eval_samples_per_second": 494.496,
+    "eval_steps_per_second": 61.812,
+    "total_flos": 985055893118976.0,
+    "train_loss": 0.3395414849986201,
+    "train_runtime": 89.4666,
+    "train_samples": 3668,
+    "train_samples_per_second": 163.994,
+    "train_steps_per_second": 10.283
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/mrpc/config.json b/hpo-examples/text-classification/mrpc/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9c4a1102a33ebe088c1c0d8e6c5672414e04c9d2
--- /dev/null
+++ b/hpo-examples/text-classification/mrpc/config.json
@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "google-bert/bert-base-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "mrpc",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "equivalent": 1,
+    "not_equivalent": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}
diff --git a/hpo-examples/text-classification/mrpc/eval_results.json b/hpo-examples/text-classification/mrpc/eval_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..6aed8d0d06c1d535003c451b29ec1718d2cc10e1
--- /dev/null
+++ b/hpo-examples/text-classification/mrpc/eval_results.json
@@ -0,0 +1,11 @@
+{
+    "epoch": 4.0,
+    "eval_accuracy": 0.8725490196078431,
+    "eval_combined_score": 0.891753961858716,
+    "eval_f1": 0.910958904109589,
+    "eval_loss": 0.5399143695831299,
+    "eval_runtime": 0.8251,
+    "eval_samples": 408,
+    "eval_samples_per_second": 494.496,
+    "eval_steps_per_second": 61.812
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/mrpc/model.safetensors b/hpo-examples/text-classification/mrpc/model.safetensors
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+size 440358464
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diff --git a/hpo-examples/text-classification/mrpc/special_tokens_map.json b/hpo-examples/text-classification/mrpc/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..a8b3208c2884c4efb86e49300fdd3dc877220cdf
--- /dev/null
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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
diff --git a/hpo-examples/text-classification/mrpc/tokenizer.json b/hpo-examples/text-classification/mrpc/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..fa2bcd3f728c1ea04d60ee761a0773a6b6aa9324
--- /dev/null
+++ b/hpo-examples/text-classification/mrpc/tokenizer.json
@@ -0,0 +1,29146 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "[PAD]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 100,
+      "content": "[UNK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
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+    "clean_text": true,
+    "handle_chinese_chars": true,
+    "strip_accents": null,
+    "lowercase": false
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+    "type": "BertPreTokenizer"
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\ No newline at end of file
diff --git a/hpo-examples/text-classification/mrpc/tokenizer_config.json b/hpo-examples/text-classification/mrpc/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..bd5a70f5066ffa2be3f1aa44710a783a881be878
--- /dev/null
+++ b/hpo-examples/text-classification/mrpc/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/hpo-examples/text-classification/mrpc/train_results.json b/hpo-examples/text-classification/mrpc/train_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b10c9abd2f6a923b3432068a856304126783ffc
--- /dev/null
+++ b/hpo-examples/text-classification/mrpc/train_results.json
@@ -0,0 +1,9 @@
+{
+    "epoch": 4.0,
+    "total_flos": 985055893118976.0,
+    "train_loss": 0.3395414849986201,
+    "train_runtime": 89.4666,
+    "train_samples": 3668,
+    "train_samples_per_second": 163.994,
+    "train_steps_per_second": 10.283
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/mrpc/trainer_state.json b/hpo-examples/text-classification/mrpc/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..b79e91622bfeccb245a6f98e596e7632fe8347f9
--- /dev/null
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diff --git a/hpo-examples/text-classification/qnli/README.md b/hpo-examples/text-classification/qnli/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..59c8c278e155f45d1a71e1086de0d8cd81e91068
--- /dev/null
+++ b/hpo-examples/text-classification/qnli/README.md
@@ -0,0 +1,73 @@
+---
+library_name: transformers
+language:
+- en
+license: apache-2.0
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+model-index:
+- name: qnli
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE QNLI
+      type: glue
+      args: qnli
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9110378912685337
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# qnli
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the GLUE QNLI dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7799
+- Accuracy: 0.9110
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/text-classification/qnli/all_results.json b/hpo-examples/text-classification/qnli/all_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..f5ffdbbf397b83a762b2045677342d80ab91ab39
--- /dev/null
+++ b/hpo-examples/text-classification/qnli/all_results.json
@@ -0,0 +1,15 @@
+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.9110378912685337,
+    "eval_loss": 0.7799292206764221,
+    "eval_runtime": 10.5795,
+    "eval_samples": 5463,
+    "eval_samples_per_second": 516.376,
+    "eval_steps_per_second": 64.559,
+    "total_flos": 7.032286628473344e+16,
+    "train_loss": 0.11521797929512122,
+    "train_runtime": 5803.3368,
+    "train_samples": 104743,
+    "train_samples_per_second": 180.488,
+    "train_steps_per_second": 5.642
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/qnli/config.json b/hpo-examples/text-classification/qnli/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..10d3b4c44c7ec0f3533d08183b802bbc411a8b9d
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+      "expressions": 11792,
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+      "##sal": 11794,
+      "Gods": 11795,
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+      "Kazakhstan": 11816,
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+      "##rz": 11819,
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+      "Guatemala": 11835,
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+      "sin": 11850,
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+      "Downtown": 11852,
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+      "##ote": 11860,
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+      "##rrell": 11862,
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+      "colleague": 11864,
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+      "prints": 11875,
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+      "##makers": 11877,
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+      "illustrations": 11880,
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+      "beats": 11883,
+      "editors": 11884,
+      "mount": 11885,
+      "##grave": 11886,
+      "Malaysian": 11887,
+      "Bombay": 11888,
+      "endorsed": 11889,
+      "##sian": 11890,
+      "##bee": 11891,
+      "applying": 11892,
+      "Religion": 11893,
+      "nautical": 11894,
+      "bomber": 11895,
+      "Na": 11896,
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+      "##rew": 11899,
+      "Cave": 11900,
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+      "Election": 11905,
+      "Hawk": 11906,
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+      "##iled": 11908,
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+      "##tland": 11910,
+      "liver": 11911,
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+      "Put": 11913,
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+      "Ella": 11915,
+      "##fect": 11916,
+      "Cal": 11917,
+      "invention": 11918,
+      "Castro": 11919,
+      "bored": 11920,
+      "##kawa": 11921,
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+      "Trinidad": 11923,
+      "NASCAR": 11924,
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+      "##pton": 11927,
+      "expenses": 11928,
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+      "##rf": 11931,
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+      "beta": 11933,
+      "parameters": 11934,
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+      "##lene": 11936,
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+      "Beat": 11938,
+      "blades": 11939,
+      "Either": 11940,
+      "##hale": 11941,
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+      "##vous": 11944,
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+      "##front": 11949,
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+      "128": 11965,
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+      "charm": 11967,
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+      "##tian": 11969,
+      "fusion": 11970,
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+      "visitor": 11972,
+      "pinned": 11973,
+      "genera": 11974,
+      "3000": 11975,
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+      "Solo": 11977,
+      "quantity": 11978,
+      "regained": 11979,
+      "platinum": 11980,
+      "shoots": 11981,
+      "narrowly": 11982,
+      "preceded": 11983,
+      "update": 11984,
+      "##ichi": 11985,
+      "equality": 11986,
+      "unaware": 11987,
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diff --git a/hpo-examples/text-classification/qqp/README.md b/hpo-examples/text-classification/qqp/README.md
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index 0000000000000000000000000000000000000000..42ac7fae3419cd2e52528d049cd9243b15b46412
--- /dev/null
+++ b/hpo-examples/text-classification/qqp/README.md
@@ -0,0 +1,79 @@
+---
+library_name: transformers
+language:
+- en
+license: apache-2.0
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+- f1
+model-index:
+- name: qqp
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE QQP
+      type: glue
+      args: qqp
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9088053425674004
+    - name: F1
+      type: f1
+      value: 0.8766435812506274
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# qqp
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the GLUE QQP dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5929
+- Accuracy: 0.9088
+- F1: 0.8766
+- Combined Score: 0.8927
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/text-classification/qqp/all_results.json b/hpo-examples/text-classification/qqp/all_results.json
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index 0000000000000000000000000000000000000000..32f567ddf62a6ca38ab4b2967a51df91290c71d3
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+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.9088053425674004,
+    "eval_combined_score": 0.8927244619090139,
+    "eval_f1": 0.8766435812506274,
+    "eval_loss": 0.5928972363471985,
+    "eval_runtime": 78.1295,
+    "eval_samples": 40430,
+    "eval_samples_per_second": 517.474,
+    "eval_steps_per_second": 64.687,
+    "total_flos": 2.4428070234989568e+17,
+    "train_loss": 0.1514742186763295,
+    "train_runtime": 20029.6511,
+    "train_samples": 363846,
+    "train_samples_per_second": 181.654,
+    "train_steps_per_second": 5.677
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/qqp/config.json b/hpo-examples/text-classification/qqp/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..922df87a073008f7dcd49afc472fa59300fadce8
--- /dev/null
+++ b/hpo-examples/text-classification/qqp/config.json
@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "google-bert/bert-base-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "qqp",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "duplicate": 1,
+    "not_duplicate": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}
diff --git a/hpo-examples/text-classification/qqp/eval_results.json b/hpo-examples/text-classification/qqp/eval_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..2e47ef03dbf27acc7915c1791b335f765287fb47
--- /dev/null
+++ b/hpo-examples/text-classification/qqp/eval_results.json
@@ -0,0 +1,11 @@
+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.9088053425674004,
+    "eval_combined_score": 0.8927244619090139,
+    "eval_f1": 0.8766435812506274,
+    "eval_loss": 0.5928972363471985,
+    "eval_runtime": 78.1295,
+    "eval_samples": 40430,
+    "eval_samples_per_second": 517.474,
+    "eval_steps_per_second": 64.687
+}
\ No newline at end of file
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new file mode 100644
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--- /dev/null
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new file mode 100644
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\ No newline at end of file
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diff --git a/hpo-examples/text-classification/requirements.txt b/hpo-examples/text-classification/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19090ab12477d9389cbc69804ef43180ce0e1f66
--- /dev/null
+++ b/hpo-examples/text-classification/requirements.txt
@@ -0,0 +1,8 @@
+accelerate >= 0.12.0
+datasets >= 1.8.0
+sentencepiece != 0.1.92
+scipy
+scikit-learn
+protobuf
+torch >= 1.3
+evaluate
\ No newline at end of file
diff --git a/hpo-examples/text-classification/rte/README.md b/hpo-examples/text-classification/rte/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d5de238679045805683d1ad2c3325933b1e46c8e
--- /dev/null
+++ b/hpo-examples/text-classification/rte/README.md
@@ -0,0 +1,73 @@
+---
+library_name: transformers
+language:
+- en
+license: apache-2.0
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+model-index:
+- name: rte
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE RTE
+      type: glue
+      args: rte
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7256317689530686
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# rte
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the GLUE RTE dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1735
+- Accuracy: 0.7256
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/text-classification/rte/all_results.json b/hpo-examples/text-classification/rte/all_results.json
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+++ b/hpo-examples/text-classification/rte/all_results.json
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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.7256317689530686,
+    "eval_loss": 1.1735286712646484,
+    "eval_runtime": 0.553,
+    "eval_samples": 277,
+    "eval_samples_per_second": 500.885,
+    "eval_steps_per_second": 63.289,
+    "total_flos": 835874173209600.0,
+    "train_loss": 0.4375361124674479,
+    "train_runtime": 69.5104,
+    "train_samples": 2490,
+    "train_samples_per_second": 179.11,
+    "train_steps_per_second": 5.611
+}
\ No newline at end of file
diff --git a/hpo-examples/text-classification/rte/config.json b/hpo-examples/text-classification/rte/config.json
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--- /dev/null
+++ b/hpo-examples/text-classification/rte/config.json
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+{
+  "_name_or_path": "google-bert/bert-base-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "rte",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "entailment": 0,
+    "not_entailment": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}
diff --git a/hpo-examples/text-classification/rte/eval_results.json b/hpo-examples/text-classification/rte/eval_results.json
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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.7256317689530686,
+    "eval_loss": 1.1735286712646484,
+    "eval_runtime": 0.553,
+    "eval_samples": 277,
+    "eval_samples_per_second": 500.885,
+    "eval_steps_per_second": 63.289
+}
\ No newline at end of file
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new file mode 100644
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--- /dev/null
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\ No newline at end of file
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diff --git a/hpo-examples/text-classification/run.sh b/hpo-examples/text-classification/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b9a7e6793d6efbb60678772a6c12026a90f8ce33
--- /dev/null
+++ b/hpo-examples/text-classification/run.sh
@@ -0,0 +1,84 @@
+#!/bin/bash
+
+
+CUDA_VISIBLE_DEVICES=0 python run_glue.py \
+    --model_name_or_path google-bert/bert-base-cased \
+    --task_name "cola" \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 32 \
+    --learning_rate 2.5e-5 \
+    --num_train_epochs 6 \
+    --output_dir "cola" \
+    --overwrite_output_dir \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+
+
+CUDA_VISIBLE_DEVICES=0 python run_glue.py \
+    --model_name_or_path google-bert/bert-base-cased \
+    --task_name "sst2" \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 64 \
+    --learning_rate 3e-5 \
+    --num_train_epochs 5 \
+    --output_dir "sst2" \
+    --overwrite_output_dir \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+
+
+CUDA_VISIBLE_DEVICES=0 python run_glue.py \
+    --model_name_or_path google-bert/bert-base-cased \
+    --task_name "mrpc" \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 16 \
+    --learning_rate 2e-5 \
+    --num_train_epochs 4 \
+    --output_dir "mrpc" \
+    --overwrite_output_dir \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+
+CUDA_VISIBLE_DEVICES=0 python run_glue.py \
+    --model_name_or_path google-bert/bert-base-cased \
+    --task_name "qqp" \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 32 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 10 \
+    --output_dir "qqp" \
+    --overwrite_output_dir \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+
+CUDA_VISIBLE_DEVICES=0 python run_glue.py \
+    --model_name_or_path google-bert/bert-base-cased \
+    --task_name "qnli" \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 32 \
+    --learning_rate 2e-5 \
+    --num_train_epochs 10 \
+    --output_dir "qnli" \
+    --overwrite_output_dir \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+
+
+CUDA_VISIBLE_DEVICES=0 python run_glue.py \
+    --model_name_or_path google-bert/bert-base-cased \
+    --task_name "rte" \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 32 \
+    --learning_rate 5e-5 \
+    --num_train_epochs 5 \
+    --output_dir "rte" \
+    --overwrite_output_dir \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
+
diff --git a/hpo-examples/text-classification/run_baseline.sh b/hpo-examples/text-classification/run_baseline.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f898850e740ef6c4a8ddd2f4f3083dd5de6496cd
--- /dev/null
+++ b/hpo-examples/text-classification/run_baseline.sh
@@ -0,0 +1,31 @@
+TASKS=("cola" "sst2" "mrpc" "qqp" "mnli" "qnli" "rte" "wnli")
+
+# Create a directory for logs in the current working directory
+LOG_DIR="./baseline"
+mkdir -p "$LOG_DIR"
+
+# Loop through each task
+for TASK_NAME in "${TASKS[@]}"; do
+    # Set epochs to 5 for MRPC and WNLI, otherwise 3
+    if [[ "$TASK_NAME" == "mrpc" || "$TASK_NAME" == "wnli" ]]; then
+        NUM_EPOCHS=5
+    else
+        NUM_EPOCHS=3
+    fi
+
+    echo "Running training for task: $TASK_NAME with $NUM_EPOCHS epochs..."
+
+    CUDA_VISIBLE_DEVICES=0 python run_glue.py \
+      --model_name_or_path google-bert/bert-base-cased \
+      --task_name $TASK_NAME \
+      --do_train \
+      --do_eval \
+      --max_seq_length 128 \
+      --per_device_train_batch_size 32 \
+      --learning_rate 2e-5 \
+      --num_train_epochs $NUM_EPOCHS \
+      --output_dir $LOG_DIR/$TASK_NAME/ \
+      --overwrite_output_dir
+
+    echo "Finished training for task: $TASK_NAME"
+done
diff --git a/hpo-examples/text-classification/run_glue.py b/hpo-examples/text-classification/run_glue.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0b5a7741eb68d51d02d7326181f093192bf1d92
--- /dev/null
+++ b/hpo-examples/text-classification/run_glue.py
@@ -0,0 +1,665 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Finetuning the library models for sequence classification on GLUE."""
+# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
+
+import logging
+import os
+import random
+import sys
+from dataclasses import dataclass, field
+from typing import Optional, List
+
+import datasets
+import evaluate
+import numpy as np
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PretrainedConfig,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+from trplib import apply_trp
+
+
+# # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+# check_min_version("4.50.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
+
+task_to_keys = {
+    "cola": ("sentence", None),
+    "mnli": ("premise", "hypothesis"),
+    "mrpc": ("sentence1", "sentence2"),
+    "qnli": ("question", "sentence"),
+    "qqp": ("question1", "question2"),
+    "rte": ("sentence1", "sentence2"),
+    "sst2": ("sentence", None),
+    "stsb": ("sentence1", "sentence2"),
+    "wnli": ("sentence1", "sentence2"),
+}
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    task_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
+    )
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    max_seq_length: int = field(
+        default=128,
+        metadata={
+            "help": (
+                "The maximum total input sequence length after tokenization. Sequences longer "
+                "than this will be truncated, sequences shorter will be padded."
+            )
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    pad_to_max_length: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether to pad all samples to `max_seq_length`. "
+                "If False, will pad the samples dynamically when batching to the maximum length in the batch."
+            )
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+                "value if set."
+            )
+        },
+    )
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the training data."}
+    )
+    validation_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the validation data."}
+    )
+    test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
+
+    def __post_init__(self):
+        if self.task_name is not None:
+            self.task_name = self.task_name.lower()
+            if self.task_name not in task_to_keys.keys():
+                raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
+        elif self.dataset_name is not None:
+            pass
+        elif self.train_file is None or self.validation_file is None:
+            raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
+        else:
+            train_extension = self.train_file.split(".")[-1]
+            assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            validation_extension = self.validation_file.split(".")[-1]
+            assert (
+                validation_extension == train_extension
+            ), "`validation_file` should have the same extension (csv or json) as `train_file`."
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
+
+    apply_trp: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to apply TRP or not."},
+    )
+    trp_depths: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "TRP depth value."
+        },
+    )
+    trp_p: Optional[float] = field(
+        default=0.1,
+        metadata={
+            "help": "TRP p value."
+        },
+    )
+    trp_lambdas: Optional[List[float]] = field(
+        default_factory=lambda: [0.4, 0.2, 0.1],
+        metadata={
+            "help": "TRP lambda values (list of floats)."
+        },
+    )
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_glue", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
+    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
+    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
+    # label if at least two columns are provided.
+    #
+    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
+    # single column. You can easily tweak this behavior (see below)
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.task_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            "nyu-mll/glue",
+            data_args.task_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+        )
+    elif data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
+        )
+    else:
+        # Loading a dataset from your local files.
+        # CSV/JSON training and evaluation files are needed.
+        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
+
+        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
+        # when you use `do_predict` without specifying a GLUE benchmark task.
+        if training_args.do_predict:
+            if data_args.test_file is not None:
+                train_extension = data_args.train_file.split(".")[-1]
+                test_extension = data_args.test_file.split(".")[-1]
+                assert (
+                    test_extension == train_extension
+                ), "`test_file` should have the same extension (csv or json) as `train_file`."
+                data_files["test"] = data_args.test_file
+            else:
+                raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
+
+        for key in data_files.keys():
+            logger.info(f"load a local file for {key}: {data_files[key]}")
+
+        if data_args.train_file.endswith(".csv"):
+            # Loading a dataset from local csv files
+            raw_datasets = load_dataset(
+                "csv",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+        else:
+            # Loading a dataset from local json files
+            raw_datasets = load_dataset(
+                "json",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+    # See more about loading any type of standard or custom dataset at
+    # https://huggingface.co/docs/datasets/loading_datasets.
+
+    # Labels
+    if data_args.task_name is not None:
+        is_regression = data_args.task_name == "stsb"
+        if not is_regression:
+            label_list = raw_datasets["train"].features["label"].names
+            num_labels = len(label_list)
+        else:
+            num_labels = 1
+    else:
+        # Trying to have good defaults here, don't hesitate to tweak to your needs.
+        is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
+        if is_regression:
+            num_labels = 1
+        else:
+            # A useful fast method:
+            # https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique
+            label_list = raw_datasets["train"].unique("label")
+            label_list.sort()  # Let's sort it for determinism
+            num_labels = len(label_list)
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        num_labels=num_labels,
+        finetuning_task=data_args.task_name,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
+    )
+    if model_args.apply_trp and training_args.do_train:
+        model = apply_trp(model, model_args.trp_depths, model_args.trp_p, model_args.trp_lambdas)
+
+    # Preprocessing the raw_datasets
+    if data_args.task_name is not None:
+        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
+    else:
+        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
+        non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
+        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
+            sentence1_key, sentence2_key = "sentence1", "sentence2"
+        else:
+            if len(non_label_column_names) >= 2:
+                sentence1_key, sentence2_key = non_label_column_names[:2]
+            else:
+                sentence1_key, sentence2_key = non_label_column_names[0], None
+
+    # Padding strategy
+    if data_args.pad_to_max_length:
+        padding = "max_length"
+    else:
+        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
+        padding = False
+
+    # Some models have set the order of the labels to use, so let's make sure we do use it.
+    label_to_id = None
+    if (
+        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
+        and data_args.task_name is not None
+        and not is_regression
+    ):
+        # Some have all caps in their config, some don't.
+        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
+        if sorted(label_name_to_id.keys()) == sorted(label_list):
+            label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
+        else:
+            logger.warning(
+                "Your model seems to have been trained with labels, but they don't match the dataset: "
+                f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
+                "\nIgnoring the model labels as a result.",
+            )
+    elif data_args.task_name is None and not is_regression:
+        label_to_id = {v: i for i, v in enumerate(label_list)}
+
+    if label_to_id is not None:
+        model.config.label2id = label_to_id
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+    elif data_args.task_name is not None and not is_regression:
+        model.config.label2id = {l: i for i, l in enumerate(label_list)}
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+
+    if data_args.max_seq_length > tokenizer.model_max_length:
+        logger.warning(
+            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
+            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+        )
+    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
+    def preprocess_function(examples):
+        # Tokenize the texts
+        args = (
+            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
+        )
+        result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
+
+        # Map labels to IDs (not necessary for GLUE tasks)
+        if label_to_id is not None and "label" in examples:
+            result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
+        return result
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        raw_datasets = raw_datasets.map(
+            preprocess_function,
+            batched=True,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on dataset",
+        )
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
+        if data_args.max_eval_samples is not None:
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+
+    if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
+        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
+        if data_args.max_predict_samples is not None:
+            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
+            predict_dataset = predict_dataset.select(range(max_predict_samples))
+
+    # Log a few random samples from the training set:
+    if training_args.do_train:
+        for index in random.sample(range(len(train_dataset)), 3):
+            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # Get the metric function
+    if data_args.task_name is not None:
+        metric = evaluate.load("glue", data_args.task_name, cache_dir=model_args.cache_dir)
+    elif is_regression:
+        metric = evaluate.load("mse", cache_dir=model_args.cache_dir)
+    else:
+        metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
+
+    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
+    # predictions and label_ids field) and has to return a dictionary string to float.
+    def compute_metrics(p: EvalPrediction):
+        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
+        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
+        result = metric.compute(predictions=preds, references=p.label_ids)
+        if len(result) > 1:
+            result["combined_score"] = np.mean(list(result.values())).item()
+        return result
+
+    # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
+    # we already did the padding.
+    if data_args.pad_to_max_length:
+        data_collator = default_data_collator
+    elif training_args.fp16:
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
+    else:
+        data_collator = None
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        compute_metrics=compute_metrics,
+        processing_class=tokenizer,
+        data_collator=data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        eval_datasets = [eval_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            valid_mm_dataset = raw_datasets["validation_mismatched"]
+            if data_args.max_eval_samples is not None:
+                max_eval_samples = min(len(valid_mm_dataset), data_args.max_eval_samples)
+                valid_mm_dataset = valid_mm_dataset.select(range(max_eval_samples))
+            eval_datasets.append(valid_mm_dataset)
+            combined = {}
+
+        for eval_dataset, task in zip(eval_datasets, tasks):
+            metrics = trainer.evaluate(eval_dataset=eval_dataset)
+
+            max_eval_samples = (
+                data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+            )
+            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+            if task == "mnli-mm":
+                metrics = {k + "_mm": v for k, v in metrics.items()}
+            if task is not None and "mnli" in task:
+                combined.update(metrics)
+
+            trainer.log_metrics("eval", metrics)
+            trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        predict_datasets = [predict_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            predict_datasets.append(raw_datasets["test_mismatched"])
+
+        for predict_dataset, task in zip(predict_datasets, tasks):
+            # Removing the `label` columns because it contains -1 and Trainer won't like that.
+            predict_dataset = predict_dataset.remove_columns("label")
+            predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
+            predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
+
+            output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
+            if trainer.is_world_process_zero():
+                with open(output_predict_file, "w") as writer:
+                    logger.info(f"***** Predict results {task} *****")
+                    writer.write("index\tprediction\n")
+                    for index, item in enumerate(predictions):
+                        if is_regression:
+                            writer.write(f"{index}\t{item:3.3f}\n")
+                        else:
+                            item = label_list[item]
+                            writer.write(f"{index}\t{item}\n")
+
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
+    if data_args.task_name is not None:
+        kwargs["language"] = "en"
+        kwargs["dataset_tags"] = "glue"
+        kwargs["dataset_args"] = data_args.task_name
+        kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hpo-examples/text-classification/sst2/README.md b/hpo-examples/text-classification/sst2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..106e07c8309d396ee578f19f812c08caee4eec61
--- /dev/null
+++ b/hpo-examples/text-classification/sst2/README.md
@@ -0,0 +1,73 @@
+---
+library_name: transformers
+language:
+- en
+license: apache-2.0
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+model-index:
+- name: sst2
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE SST2
+      type: glue
+      args: sst2
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9254587155963303
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# sst2
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the GLUE SST2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3458
+- Accuracy: 0.9255
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 64
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu118
+- Datasets 3.3.1
+- Tokenizers 0.21.0
diff --git a/hpo-examples/text-classification/sst2/all_results.json b/hpo-examples/text-classification/sst2/all_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..acfd2606590aa2b6ce0fc6ab70ea5f36f9bb0904
--- /dev/null
+++ b/hpo-examples/text-classification/sst2/all_results.json
@@ -0,0 +1,15 @@
+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.9254587155963303,
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+      "Gods": 11795,
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+      "Guatemala": 11835,
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+      "Downtown": 11852,
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+      "##ote": 11860,
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+      "##rrell": 11862,
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+      "colleague": 11864,
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+      "prints": 11875,
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+      "illustrations": 11880,
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+      "mount": 11885,
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+      "Malaysian": 11887,
+      "Bombay": 11888,
+      "endorsed": 11889,
+      "##sian": 11890,
+      "##bee": 11891,
+      "applying": 11892,
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+      "nautical": 11894,
+      "bomber": 11895,
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+      "Election": 11905,
+      "Hawk": 11906,
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+      "##tland": 11910,
+      "liver": 11911,
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+      "Put": 11913,
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+      "Ella": 11915,
+      "##fect": 11916,
+      "Cal": 11917,
+      "invention": 11918,
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+      "##kawa": 11921,
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+      "##rf": 11931,
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+      "beta": 11933,
+      "parameters": 11934,
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+      "##lene": 11936,
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+      "blades": 11939,
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+      "##front": 11949,
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+      "##ques": 11962,
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new file mode 100644
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+##：
diff --git a/hpo-examples/text-classification/trplib.py b/hpo-examples/text-classification/trplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0fb33879b1ab05f53e0b8e7068729abbb96146
--- /dev/null
+++ b/hpo-examples/text-classification/trplib.py
@@ -0,0 +1,1181 @@
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from torchvision.models.mobilenetv2 import MobileNetV2
+from torchvision.models.resnet import ResNet
+from torchvision.models.efficientnet import EfficientNet
+from torchvision.models.vision_transformer import VisionTransformer
+from torchvision.models.segmentation.fcn import FCN
+from torchvision.models.segmentation.deeplabv3 import DeepLabV3
+
+import transformers
+from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, CausalLMOutput, Seq2SeqLMOutput
+
+from typing import Optional, Tuple, List, Union, Callable
+from collections import OrderedDict
+import types
+
+
+def trp_criterion(trp_blocks: nn.ModuleList, shared_head: Callable, criterion: Callable, lambdas: List[float], hidden_states: Tensor, logits: Tensor, targets: Tensor, loss_normalization=False):
+    loss, mask = criterion(logits, targets)
+    if loss_normalization:
+        coeff = loss.detach()
+
+    embeds = [hidden_states]
+    predictions = []
+    for k, c in enumerate(lambdas):
+        embeds.append(trp_blocks[k](embeds[-1]))
+        predictions.append(shared_head(embeds[-1]))
+        replica_loss, mask = criterion(predictions[-1], targets, mask)
+        loss += c * replica_loss
+    
+    if loss_normalization:
+        with torch.no_grad():
+            coeff = torch.exp(coeff) / torch.exp(loss.detach())
+        loss = coeff * loss
+    
+    return loss
+
+
+class TPBlock(nn.Module):
+    def __init__(self, depths: int, in_features: int, p: float, dim=-1):
+        super(TPBlock, self).__init__()
+
+        self.dropout = nn.Dropout(p)
+
+        self.cdim = dim
+
+        blocks = []
+        for _ in range(depths):
+            blocks.append(nn.Linear(in_features, in_features))
+            nn.init.constant_(blocks[-1].weight, 0.0)
+            nn.init.constant_(blocks[-1].bias, 0.0)
+            blocks.append(nn.ReLU())
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        x = self.dropout(x)
+        if self.cdim == -1:
+            x = x + self.blocks(x)
+        else:
+            x = x + torch.movedim(self.blocks(torch.movedim(x, self.cdim, -1)), -1, self.cdim)
+        return x
+    
+
+class Config:
+    @staticmethod
+    def gen_criterion(*args, **kwargs):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor.
+                target (Tensor): Target labels.
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor with the same shape of target.
+            """
+            pass
+        return func
+    
+    @staticmethod
+    def gen_shared_head(*args, **kwargs):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States tensor.
+
+            Returns:
+                logits (Tensor): Logits tensor.
+            """
+            pass
+        return func
+
+    @staticmethod
+    def forward(*args, **kwargs):
+        pass
+
+
+# Wav2Vec2 for Audio Classification
+class Wav2Vec2ForSequenceClassificationConfig(Config): 
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self, attention_mask):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+            output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            if self.config.use_weighted_layer_sum:
+                hidden_states = outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION]
+                hidden_states = torch.stack(hidden_states, dim=1)
+                norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+                hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+            else:
+                hidden_states = outputs[0]
+
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                shared_head = Wav2Vec2ForSequenceClassificationConfig.gen_shared_head(self, attention_mask)
+                criterion = Wav2Vec2ForSequenceClassificationConfig.gen_criterion()
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states, logits.view(-1, self.config.num_labels),  labels.view(-1), loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# MobileNetV2 for Image Classification
+class MobileNetV2Config(Config):
+    @staticmethod
+    def gen_criterion(label_smoothing=0.0, top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B] or [B, C].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            label = torch.argmax(target, dim=1) if label_smoothing > 0.0 else target
+                
+            unmasked_loss = F.cross_entropy(input, label, reduction="none", label_smoothing=label_smoothing)
+            if mask is None:
+                mask = torch.ones_like(unmasked_loss, dtype=torch.float32, device=target.device)
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=-1)
+                mask = mask * torch.eq(topk_indices, label[:, None]).any(dim=-1).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = MobileNetV2Config.gen_shared_head(self)
+                criterion = MobileNetV2Config.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ResNet for Image Classification
+class ResNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.fc(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.conv1(images)
+            x = self.bn1(x)
+            x = self.relu(x)
+            x = self.maxpool(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.fc(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = ResNetConfig.gen_shared_head(self)
+                criterion = ResNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# EfficientNet for Image Classification
+class EfficientNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = EfficientNetConfig.gen_shared_head(self)
+                criterion = EfficientNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ViT for Image Classification
+class VisionTransformerConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.heads(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self._process_input(images)
+            n = x.shape[0]
+            batch_class_token = self.class_token.expand(n, -1, -1)
+            x = torch.cat([batch_class_token, x], dim=1)
+            x = self.encoder(x)
+            x = x[:, 0]
+
+            logits = self.heads(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = VisionTransformerConfig.gen_shared_head(self)
+                criterion = VisionTransformerConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# Bert for Question Answering
+class BertForQuestionAnsweringConfig(Config): 
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target: List[Tensor], mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C, 2].
+                target (List[Tensor]): 
+                    Start Positions of shape [B].
+                    End Positions of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            start_positions, end_positions = target
+
+            if mask is None:
+                mask = torch.ones_like(start_positions, dtype=torch.float32, device=start_positions.device)
+
+            start_logits, end_logits = input.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            masked_start_losses = F.cross_entropy(start_logits, start_positions, ignore_index=ignored_index, reduction="none")
+            start_loss = torch.sum(mask * masked_start_losses) / (torch.sum(mask) + 1e-6)
+            masked_end_losses = F.cross_entropy(end_logits, end_positions, ignore_index=ignored_index, reduction="none")
+            end_loss = torch.sum(mask * masked_end_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(start_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, start_positions[:, None]).any(dim=1).to(start_logits.dtype)
+                topk_values, topk_indices = torch.topk(end_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, end_positions[:, None]).any(dim=1).to(end_logits.dtype)
+
+            return (start_loss + end_loss) / 2, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.qa_outputs(hidden_states)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            start_positions: Optional[torch.Tensor] = None,
+            end_positions: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+            r"""
+            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the start of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the end of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            sequence_output = outputs[0]
+
+            logits = self.qa_outputs(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                shared_head = BertForQuestionAnsweringConfig.gen_shared_head(self)
+                criterion = BertForQuestionAnsweringConfig.gen_criterion()
+                total_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, [start_positions, end_positions], loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+    
+
+# FCN for Semantic Segmentation
+class FCNConfig(Config):
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input Tensor: input tensor of shape [B, C, H, W].
+                target (Tensor): Target labels of shape [B, H, W].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B, H, W].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+                
+            masked_loss = F.cross_entropy(input, target, ignore_index=255, reduction="none")
+            loss = torch.sum(mask * masked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, target[:, None, :, :]).any(dim=1).to(input.dtype)
+                # mask = mask * torch.eq(torch.argmax(x, dim=1), target).to(x.dtype)
+                
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_out_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+    
+    @staticmethod
+    def gen_aux_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.aux_classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            input_shape = images.shape[-2:]
+            # contract: features is a dict of tensors
+            features = self.backbone(images)
+
+            result = OrderedDict()
+            x = features["out"]
+            x = self.classifier(x)
+            x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            result["out"] = x
+
+            if self.aux_classifier is not None:
+                x = features["aux"]
+                x = self.aux_classifier(x)
+                x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+                result["aux"] = x
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                out_shared_head = FCNConfig.gen_out_shared_head(self, input_shape)
+                aux_shared_head = FCNConfig.gen_aux_shared_head(self, input_shape)
+                criterion = FCNConfig.gen_criterion(top_k)
+                out_loss = trp_criterion(self.out_trp_blocks, out_shared_head, criterion, lambdas, features["out"], result["out"], targets, loss_normalization)
+                aux_loss = trp_criterion(self.aux_trp_blocks, aux_shared_head, criterion, lambdas, features["aux"], result["aux"], targets, loss_normalization)
+                loss = out_loss + 0.5 * aux_loss
+                return result, loss
+            return result
+        return func
+
+
+# DeepLabV3Config for Semantic Segmentation
+class DeepLabV3Config(FCNConfig):
+    pass
+
+
+# Bert for Text Classification
+class BertForSequenceClassificationConfig(Config): 
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, pooled_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Boberta for Text Classification
+class RobertaForSequenceClassificationConfig(BertForSequenceClassificationConfig): 
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.roberta(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            sequence_output = outputs[0]
+            logits = self.classifier(sequence_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                # move labels to correct device to enable model parallelism
+                labels = labels.to(logits.device)
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Wav2Vec2 for Speech Recognition
+class Wav2Vec2ForCTCConfig(Config):
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def greedy_decode_ctc(
+        log_probs: torch.Tensor,
+        input_lengths: torch.Tensor,
+        blank_token_id: int,
+        target_lengths: torch.Tensor
+    ):
+        """
+        Convert logits to flattened predictions that match the shape of flattened_targets.
+
+        Args:
+            log_probs: [B, L, V] - log-softmax output
+            input_lengths: [B] - actual length of each input
+            blank_token_id: int - index of blank token
+            target_lengths: [B] - used to determine how many predictions to keep per sample
+
+        Returns:
+            flattened_predictions: 1D tensor, same total length as sum(target_lengths)
+        """
+        batch_size = log_probs.size(0)
+        decoded_all = []
+
+        predicted_ids = log_probs.argmax(dim=-1)  # [B, L]
+
+        for i in range(batch_size):
+            pred = predicted_ids[i][:input_lengths[i]]  # [Li]
+            prev = None
+            decoded = []
+            for token in pred:
+                token = token.item()
+                if token != blank_token_id and token != prev:
+                    decoded.append(token)
+                prev = token
+            # Trim or pad to match target_lengths[i]
+            tgt_len = target_lengths[i].item()
+            if len(decoded) >= tgt_len:
+                decoded = decoded[:tgt_len]
+            else:
+                decoded = decoded + [blank_token_id] * (tgt_len - len(decoded))  # pad with blank
+            decoded_all.extend(decoded)
+
+        return torch.tensor(decoded_all, dtype=torch.long, device=log_probs.device)  # shape: [sum(target_lengths)]
+
+    @staticmethod
+    def gen_criterion(input_lengths: Tensor, pad_token_id: int, ctc_zero_infinity: bool):
+        def func(logits: Tensor, labels: Tensor, mask=None):
+            """
+            Args:
+                logits (Tensor): Log Probablities of shape [B, L, V].
+                labels (Tensor): Flattened Targets of shape [B, L'].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(input_lengths, dtype=torch.float32, device=input_lengths.device)
+
+            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            with torch.backends.cudnn.flags(enabled=False):
+                masked_losses = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=pad_token_id, reduction="none", zero_infinity=ctc_zero_infinity)
+                loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                thres = 0.5
+                flattened_predictions = Wav2Vec2ForCTCConfig.greedy_decode_ctc(
+                    log_probs.transpose(0, 1),  # [B, T, V]
+                    input_lengths=input_lengths,
+                    blank_token_id=pad_token_id,
+                    target_lengths=target_lengths
+                )
+                token_wise_mask =  torch.eq(flattened_predictions, flattened_targets).to(flattened_targets.dtype)
+                segment_ids = torch.arange(len(target_lengths), device=target_lengths.device).repeat_interleave(target_lengths)
+                sequence_wise_mask = torch.zeros(len(target_lengths), dtype=target_lengths.dtype, device=token_wise_mask.device).scatter_add(0, segment_ids, token_wise_mask)
+                mask = mask * torch.ge(sequence_wise_mask, thres * target_lengths).to(flattened_targets.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.lm_head(hidden_states)
+            # log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, CausalLMOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
+                Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
+                the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
+                All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
+                config.vocab_size - 1]`.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None and labels.max() >= self.config.vocab_size:
+                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            hidden_states = self.dropout(hidden_states)
+
+            logits = self.lm_head(hidden_states)
+
+            loss = None
+            if labels is not None:
+                # retrieve loss input_lengths from attention_mask
+                attention_mask = (
+                    attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
+                )
+                input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+                shared_head = Wav2Vec2ForCTCConfig.gen_shared_head(self)
+                criterion = Wav2Vec2ForCTCConfig.gen_criterion(input_lengths, self.config.pad_token_id, self.config.ctc_zero_infinity)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states,  logits, labels, loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForCTCConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return CausalLMOutput(
+                loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+            )
+        return func
+
+
+# MBart for Translation
+class MBartForConditionalGenerationConfig(Config): 
+    @staticmethod
+    def gen_criterion(vocab_size: int, top_k=1):
+        def func(logits, labels, mask=None):
+            """
+            Args:
+                logits (Tensor): Logits tensor of shape [B, L, V].
+                labels (Tensor): Target labels of shape [B, L].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(labels.view(-1), dtype=torch.float32, device=labels.device)
+                
+            masked_losses = F.cross_entropy(logits.view(-1, vocab_size), labels.view(-1), reduction="none")
+            loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(logits.view(-1, vocab_size), top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, labels.view(-1, 1)).any(dim=1).to(logits.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, L].
+            """
+            logits = self.lm_head(hidden_states) + self.final_logits_bias
+            return logits
+        return func
+    
+    @staticmethod
+    def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+        """
+        Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
+        have a single `decoder_start_token_id` in contrast to other Bart-like models.
+        """
+        prev_output_tokens = input_ids.clone()
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+        index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+        decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+        prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+        prev_output_tokens[:, 0] = decoder_start_tokens
+
+        return prev_output_tokens
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            Returns:
+
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None:
+                # if use_cache:
+                #     logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+                use_cache = False
+                if decoder_input_ids is None and decoder_inputs_embeds is None:
+                    decoder_input_ids = MBartForConditionalGenerationConfig.shift_tokens_right(labels, self.config.pad_token_id)
+
+            outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=decoder_input_ids,
+                encoder_outputs=encoder_outputs,
+                decoder_attention_mask=decoder_attention_mask,
+                head_mask=head_mask,
+                decoder_head_mask=decoder_head_mask,
+                cross_attn_head_mask=cross_attn_head_mask,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+            masked_lm_loss = None
+            if labels is not None:
+                shared_head = MBartForConditionalGenerationConfig.gen_shared_head(self)
+                criterion = MBartForConditionalGenerationConfig.gen_criterion(self.config.vocab_size)
+                masked_lm_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, outputs[0],  lm_logits, labels, loss_normalization)
+
+            if not return_dict:
+                output = (lm_logits,) + outputs[1:]
+                return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+            return Seq2SeqLMOutput(
+                loss=masked_lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                decoder_hidden_states=outputs.decoder_hidden_states,
+                decoder_attentions=outputs.decoder_attentions,
+                cross_attentions=outputs.cross_attentions,
+                encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+                encoder_hidden_states=outputs.encoder_hidden_states,
+                encoder_attentions=outputs.encoder_attentions,
+            )
+        return func
+
+
+def apply_trp(model, depths: int, p: float, lambdas: List[float], **kwargs):
+    if isinstance(model, transformers.Wav2Vec2ForSequenceClassification):
+        print("✅ Applying TRP to Wav2Vec2 for Audio Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, MobileNetV2):
+        print("✅ Applying TRP to MobileNetV2 for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(MobileNetV2Config.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, ResNet):
+        print("✅ Applying TRP to ResNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p) for _ in lambdas])
+        model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, EfficientNet):
+        print("✅ Applying TRP to EfficientNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(EfficientNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, VisionTransformer):
+        print("✅ Applying TRP to VisionTransformer for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(VisionTransformerConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, transformers.BertForQuestionAnswering):
+        print("✅ Applying TRP to Bert for Question Answering...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForQuestionAnsweringConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, FCN):
+        print("✅ Applying TRP to FCN for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(FCNConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, DeepLabV3):
+        print("✅ Applying TRP to DeepLabV3 for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(DeepLabV3Config.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, transformers.BertForSequenceClassification):
+        print("✅ Applying TRP to Bert for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.RobertaForSequenceClassification):
+        print("✅ Applying TRP to Roberta for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(RobertaForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.Wav2Vec2ForCTC):
+        print("✅ Applying TRP to Wav2Vec2 for Speech Recognition...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForCTCConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.MBartForConditionalGeneration):
+        print("✅ Applying TRP to MBart for Translation...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(MBartForConditionalGenerationConfig.gen_forward(lambdas, False), model)
+    else:
+        torch._assert(
+            isinstance(model, transformers.Wav2Vec2ForSequenceClassification), 
+            "The model should be an object of [`Wav2Vec2ForSequenceClassification`].")
+
+    return model
\ No newline at end of file
diff --git a/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/files/output.log b/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/files/requirements.txt b/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2af33488b2e791ae2fd8ea316123523c2c20d7ba
--- /dev/null
+++ b/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/files/requirements.txt
@@ -0,0 +1,268 @@
+shellingham==1.5.4
+opencensus==0.11.4
+nvidia-nvtx-cu12==12.4.127
+opentelemetry-semantic-conventions-ai==0.4.9
+iniconfig==2.1.0
+sglang==0.4.6.post1
+rich==14.0.0
+megatron-core==0.12.0rc3
+opencv-fixer==0.2.5
+fastapi==0.115.12
+fastapi-cli==0.0.7
+opentelemetry-exporter-otlp-proto-grpc==1.26.0
+airportsdata==20250523
+pure_eval==0.2.3
+python-dateutil==2.9.0.post0
+setproctitle==1.3.6
+opentelemetry-proto==1.26.0
+ray==2.47.0
+httpcore==1.0.9
+pydantic==2.11.5
+python-dotenv==1.1.0
+tomli==2.2.1
+email_validator==2.2.0
+torchvision==0.21.0+cu124
+anthropic==0.54.0
+av==14.4.0
+codetiming==1.4.0
+async-timeout==5.0.1
+nvidia-ml-py==12.575.51
+omegaconf==2.3.0
+uvicorn==0.34.3
+cffi==1.17.1
+nvidia-cuda-nvrtc-cu12==12.4.127
+transformers==4.51.1
+msgpack==1.1.0
+networkx==3.3
+llguidance==0.7.29
+ipython==8.37.0
+opentelemetry-exporter-prometheus==0.55b1
+frozenlist==1.7.0
+opentelemetry-exporter-otlp==1.26.0
+propcache==0.3.2
+flashinfer-python==0.2.2.post1+cu124torch2.6
+pycparser==2.22
+scikit-learn==1.7.0
+pyasn1_modules==0.4.2
+pytest==8.4.0
+cuda-bindings==12.9.0
+nvidia-cuda-cupti-cu12==12.4.127
+sentry-sdk==2.29.1
+idna==3.10
+opencensus-context==0.1.3
+aiosignal==1.3.2
+nanobind==2.7.0
+annotated-types==0.7.0
+rpds-py==0.25.1
+matplotlib-inline==0.1.7
+nvidia-cublas-cu12==12.4.5.8
+markdown-it-py==3.0.0
+hf-xet==1.1.3
+audioread==3.0.1
+cloudpickle==3.1.1
+hf_transfer==0.1.9
+prometheus_client==0.22.1
+wheel==0.45.1
+orjson==3.10.18
+distlib==0.3.9
+fsspec==2024.6.1
+pyasn1==0.6.1
+fastrlock==0.8.3
+nodeenv==1.9.1
+llvmlite==0.44.0
+pynvml==12.0.0
+filelock==3.18.0
+googleapis-common-protos==1.70.0
+py-cpuinfo==9.0.0
+click==8.2.1
+nvidia-cufft-cu12==11.2.1.3
+nvidia-curand-cu12==10.3.5.147
+watchfiles==1.0.5
+exceptiongroup==1.3.0
+triton==3.2.0
+soxr==0.5.0.post1
+executing==2.2.0
+grpcio==1.73.0
+pexpect==4.9.0
+litellm==1.72.4
+cuda-python==12.9.0
+wrapt==1.17.2
+numpy==1.26.4
+pydantic_core==2.33.2
+diskcache==5.6.3
+openai==1.86.0
+cupy-cuda12x==13.4.1
+uvloop==0.21.0
+anyio==4.9.0
+scipy==1.15.3
+antlr4-python3-runtime==4.9.3
+python-multipart==0.0.20
+packaging==25.0
+pylatexenc==2.10
+outlines_core==0.1.26
+sniffio==1.3.1
+dill==0.3.8
+ninja==1.11.1.4
+tzdata==2025.2
+jiter==0.10.0
+google-auth==2.40.3
+httptools==0.6.4
+websockets==15.0.1
+MarkupSafe==2.1.5
+lazy_loader==0.4
+sgl-kernel==0.1.0
+ruff==0.11.13
+nvidia-nccl-cu12==2.21.5
+astor==0.8.1
+sympy==1.13.1
+prometheus-fastapi-instrumentator==7.1.0
+mpmath==1.3.0
+jsonschema-specifications==2025.4.1
+datasets==3.6.0
+requests==2.32.4
+Pygments==2.19.1
+traitlets==5.14.3
+torch_memory_saver==0.0.6
+wcwidth==0.2.13
+vllm==0.8.5.post1
+py-spy==0.4.0
+liger_kernel==0.5.10
+pyarrow==20.0.0
+wandb==0.20.1
+numba==0.61.2
+psutil==7.0.0
+pandas==2.3.0
+torch==2.6.0+cu124
+multiprocess==0.70.16
+partial-json-parser==0.2.1.1.post5
+xgrammar==0.1.18
+PyYAML==6.0.2
+cachetools==5.5.2
+importlib_metadata==8.0.0
+h11==0.16.0
+einops==0.8.1
+python-json-logger==3.3.0
+peft==0.15.2
+threadpoolctl==3.6.0
+mdurl==0.1.2
+colorful==0.5.6
+typing-inspection==0.4.1
+charset-normalizer==3.4.2
+interegular==0.3.3
+opentelemetry-api==1.26.0
+google-api-core==2.25.0
+pycountry==24.6.1
+pluggy==1.6.0
+nvidia-cusparselt-cu12==0.6.2
+blake3==1.0.5
+pycocotools==2.0.10
+tiktoken==0.9.0
+aiohappyeyeballs==2.6.1
+torchaudio==2.6.0+cu124
+asttokens==3.0.0
+lark==1.2.2
+hydra-core==1.3.2
+yarl==1.20.1
+pip==25.1
+httpx==0.28.1
+librosa==0.11.0
+torchao==0.11.0
+decord==0.6.0
+tqdm==4.67.1
+opencv-python-headless==4.11.0.86
+distro==1.9.0
+decorator==5.2.1
+multidict==6.4.4
+six==1.17.0
+pytz==2025.2
+stack-data==0.6.3
+virtualenv==20.31.2
+pybind11==2.13.6
+platformdirs==4.3.8
+certifi==2025.4.26
+urllib3==2.4.0
+typing_extensions==4.14.0
+qwen-vl-utils==0.0.11
+torchdata==0.11.0
+typer==0.16.0
+starlette==0.46.2
+pooch==1.8.2
+smart-open==7.1.0
+referencing==0.36.2
+attrs==25.3.0
+pillow==11.0.0
+parso==0.8.4
+msgspec==0.19.0
+proto-plus==1.26.1
+opentelemetry-exporter-otlp-proto-http==1.26.0
+opencv-python==4.11.0.86
+jsonschema==4.24.0
+Deprecated==1.2.18
+opentelemetry-sdk==1.26.0
+pyzmq==26.4.0
+nvidia-cusolver-cu12==11.6.1.9
+dnspython==2.7.0
+GitPython==3.1.44
+nvidia-cusparse-cu12==12.3.1.170
+soundfile==0.13.1
+nest-asyncio==1.6.0
+optree==0.16.0
+opentelemetry-semantic-conventions==0.47b0
+xformers==0.0.29.post2
+transformer_engine==2.2.0+c55e425
+mistral_common==1.6.0
+identify==2.6.12
+tensordict==0.6.2
+jedi==0.19.2
+Jinja2==3.1.6
+regex==2024.11.6
+sentencepiece==0.2.0
+joblib==1.5.1
+setuptools==78.1.1
+depyf==0.18.0
+compressed-tensors==0.9.3
+rsa==4.9.1
+ptyprocess==0.7.0
+gitdb==4.0.12
+safetensors==0.5.3
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+modelscope==1.26.0
+aiohttp==3.12.12
+evaluate==0.4.3
+aiohttp-cors==0.8.1
+nvidia-cuda-runtime-cu12==12.4.127
+lm-format-enforcer==0.10.11
+cfgv==3.4.0
+pyext==0.7
+xxhash==3.5.0
+inflect==7.3.1
+platformdirs==4.2.2
+wheel==0.45.1
+typing_extensions==4.12.2
+autocommand==2.2.2
+packaging==24.2
+importlib_metadata==8.0.0
+backports.tarfile==1.2.0
+jaraco.text==3.12.1
+jaraco.collections==5.1.0
+jaraco.context==5.3.0
+jaraco.functools==4.0.1
+typeguard==4.3.0
+tomli==2.0.1
+more-itertools==10.3.0
+zipp==3.19.2
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new file mode 100644
index 0000000000000000000000000000000000000000..d7be177a5cc84777b06e06ec0578c6ad882b3fb6
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+++ b/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/files/wandb-metadata.json
@@ -0,0 +1,87 @@
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+      "cudaCores": 10752,
+      "architecture": "Ampere",
+      "uuid": "GPU-e910c89b-dab8-1b11-7c26-c247b97829f1"
+    },
+    {
+      "name": "NVIDIA RTX A6000",
+      "memoryTotal": "51527024640",
+      "cudaCores": 10752,
+      "architecture": "Ampere",
+      "uuid": "GPU-313ba7c7-bf3e-517b-ed2f-cc70075d6a2a"
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+      "uuid": "GPU-83aa6e6e-5f86-7b0e-376a-74a673630896"
+    }
+  ],
+  "cudaVersion": "12.4"
+}
\ No newline at end of file
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new file mode 100644
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diff --git a/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/run-0wkxfsex.wandb b/hpo-examples/text-classification/wandb/offline-run-20250611_203148-0wkxfsex/run-0wkxfsex.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/hpo-examples/translation/README.md b/hpo-examples/translation/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..74ca16ccb0bf630edc4b5b63099749686233c8ef
--- /dev/null
+++ b/hpo-examples/translation/README.md
@@ -0,0 +1,211 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+## Translation
+
+This directory contains examples for finetuning and evaluating transformers on translation tasks.
+Please tag @patil-suraj with any issues/unexpected behaviors, or send a PR!
+For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md).
+For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/blob/main/examples/legacy/seq2seq).
+
+### Supported Architectures
+
+- `BartForConditionalGeneration`
+- `FSMTForConditionalGeneration` (translation only)
+- `MBartForConditionalGeneration`
+- `MarianMTModel`
+- `PegasusForConditionalGeneration`
+- `T5ForConditionalGeneration`
+- `MT5ForConditionalGeneration`
+
+`run_translation.py` is a lightweight examples of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
+
+For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets#json-files
+and you also will find examples of these below.
+
+
+## With Trainer
+
+Here is an example of a translation fine-tuning with a MarianMT model:
+
+```bash
+python examples/pytorch/translation/run_translation.py \
+    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
+    --do_train \
+    --do_eval \
+    --source_lang en \
+    --target_lang ro \
+    --dataset_name wmt16 \
+    --dataset_config_name ro-en \
+    --output_dir /tmp/tst-translation \
+    --per_device_train_batch_size=4 \
+    --per_device_eval_batch_size=4 \
+    --overwrite_output_dir \
+    --predict_with_generate
+```
+
+MBart and some T5 models require special handling.
+
+T5 models `google-t5/t5-small`, `google-t5/t5-base`, `google-t5/t5-large`, `google-t5/t5-3b` and `google-t5/t5-11b` must use an additional argument: `--source_prefix "translate {source_lang} to {target_lang}"`. For example:
+
+```bash
+python examples/pytorch/translation/run_translation.py \
+    --model_name_or_path google-t5/t5-small \
+    --do_train \
+    --do_eval \
+    --source_lang en \
+    --target_lang ro \
+    --source_prefix "translate English to Romanian: " \
+    --dataset_name wmt16 \
+    --dataset_config_name ro-en \
+    --output_dir /tmp/tst-translation \
+    --per_device_train_batch_size=4 \
+    --per_device_eval_batch_size=4 \
+    --overwrite_output_dir \
+    --predict_with_generate
+```
+
+If you get a terrible BLEU score, make sure that you didn't forget to use the `--source_prefix` argument.
+
+For the aforementioned group of T5 models it's important to remember that if you switch to a different language pair, make sure to adjust the source and target values in all 3 language-specific command line argument: `--source_lang`, `--target_lang` and `--source_prefix`.
+
+MBart models require a different format for `--source_lang` and `--target_lang` values, e.g. instead of `en` it expects `en_XX`, for `ro` it expects `ro_RO`. The full MBart specification for language codes can be found [here](https://huggingface.co/facebook/mbart-large-cc25). For example:
+
+```bash
+python examples/pytorch/translation/run_translation.py \
+    --model_name_or_path facebook/mbart-large-en-ro  \
+    --do_train \
+    --do_eval \
+    --dataset_name wmt16 \
+    --dataset_config_name ro-en \
+    --source_lang en_XX \
+    --target_lang ro_RO \
+    --output_dir /tmp/tst-translation \
+    --per_device_train_batch_size=4 \
+    --per_device_eval_batch_size=4 \
+    --overwrite_output_dir \
+    --predict_with_generate
+ ```
+
+And here is how you would use the translation finetuning on your own files, after adjusting the
+values for the arguments `--train_file`, `--validation_file` to match your setup:
+
+```bash
+python examples/pytorch/translation/run_translation.py \
+    --model_name_or_path google-t5/t5-small \
+    --do_train \
+    --do_eval \
+    --source_lang en \
+    --target_lang ro \
+    --source_prefix "translate English to Romanian: " \
+    --dataset_name wmt16 \
+    --dataset_config_name ro-en \
+    --train_file path_to_jsonlines_file \
+    --validation_file path_to_jsonlines_file \
+    --output_dir /tmp/tst-translation \
+    --per_device_train_batch_size=4 \
+    --per_device_eval_batch_size=4 \
+    --overwrite_output_dir \
+    --predict_with_generate
+```
+
+The task of translation supports only custom JSONLINES files, with each line being a dictionary with a key `"translation"` and its value another dictionary whose keys is the language pair. For example:
+
+```json
+{ "translation": { "en": "Others have dismissed him as a joke.", "ro": "Alții l-au numit o glumă." } }
+{ "translation": { "en": "And some are holding out for an implosion.", "ro": "Iar alții așteaptă implozia." } }
+```
+Here the languages are Romanian (`ro`) and English (`en`).
+
+If you want to use a pre-processed dataset that leads to high BLEU scores, but for the `en-de` language pair, you can use `--dataset_name stas/wmt14-en-de-pre-processed`, as following:
+
+```bash
+python examples/pytorch/translation/run_translation.py \
+    --model_name_or_path google-t5/t5-small \
+    --do_train \
+    --do_eval \
+    --source_lang en \
+    --target_lang de \
+    --source_prefix "translate English to German: " \
+    --dataset_name stas/wmt14-en-de-pre-processed \
+    --output_dir /tmp/tst-translation \
+    --per_device_train_batch_size=4 \
+    --per_device_eval_batch_size=4 \
+    --overwrite_output_dir \
+    --predict_with_generate
+ ```
+
+## With Accelerate
+
+Based on the script [`run_translation_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py).
+
+Like `run_translation.py`, this script allows you to fine-tune any of the models supported on a
+translation task, the main difference is that this
+script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.
+
+It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
+or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by
+the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
+after installing it:
+
+```bash
+pip install git+https://github.com/huggingface/accelerate
+```
+
+then
+
+```bash
+python run_translation_no_trainer.py \
+    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
+    --source_lang en \
+    --target_lang ro \
+    --dataset_name wmt16 \
+    --dataset_config_name ro-en \
+    --output_dir ~/tmp/tst-translation
+```
+
+You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run
+
+```bash
+accelerate config
+```
+
+and reply to the questions asked. Then
+
+```bash
+accelerate test
+```
+
+that will check everything is ready for training. Finally, you can launch training with
+
+```bash
+accelerate launch run_translation_no_trainer.py \
+    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
+    --source_lang en \
+    --target_lang ro \
+    --dataset_name wmt16 \
+    --dataset_config_name ro-en \
+    --output_dir ~/tmp/tst-translation
+```
+
+This command is the same and will work for:
+
+- a CPU-only setup
+- a setup with one GPU
+- a distributed training with several GPUs (single or multi node)
+- a training on TPUs
+
+Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
diff --git a/hpo-examples/translation/requirements.txt b/hpo-examples/translation/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9c9257430c06a832445db13b2446c6ac0bbadbcc
--- /dev/null
+++ b/hpo-examples/translation/requirements.txt
@@ -0,0 +1,8 @@
+accelerate >= 0.12.0
+datasets >= 1.8.0
+sentencepiece != 0.1.92
+protobuf
+sacrebleu >= 1.4.12
+py7zr
+torch >= 1.3
+evaluate
\ No newline at end of file
diff --git a/hpo-examples/translation/run.sh b/hpo-examples/translation/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..51d8fe1be31557d3d8ad80be7fd1c5e00ecfe86b
--- /dev/null
+++ b/hpo-examples/translation/run.sh
@@ -0,0 +1,15 @@
+# en-ro/ro-en: 38.5/38.5 ->
+CUDA_VISIBLE_DEVICES=0 python run_translation.py \
+    --model_name_or_path facebook/mbart-large-en-ro  \
+    --do_train \
+    --do_eval \
+    --dataset_name wmt16 \
+    --dataset_config_name ro-en \
+    --source_lang en_XX \
+    --target_lang ro_RO \
+    --output_dir ./baseline \
+    --per_device_train_batch_size=4 \
+    --per_device_eval_batch_size=4 \
+    --overwrite_output_dir \
+    --predict_with_generate \
+    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1
\ No newline at end of file
diff --git a/hpo-examples/translation/run_translation.py b/hpo-examples/translation/run_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5a94d1745172a4bc4b46cae4c2bf1ba00379c3a
--- /dev/null
+++ b/hpo-examples/translation/run_translation.py
@@ -0,0 +1,724 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for sequence to sequence.
+"""
+# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Optional, List
+
+import datasets
+import evaluate
+import numpy as np
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    DataCollatorForSeq2Seq,
+    HfArgumentParser,
+    M2M100Tokenizer,
+    MBart50Tokenizer,
+    MBart50TokenizerFast,
+    MBartTokenizer,
+    MBartTokenizerFast,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from trplib import apply_trp
+
+
+# # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+# check_min_version("4.50.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+# A list of all multilingual tokenizer which require src_lang and tgt_lang attributes.
+MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer]
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+
+    apply_trp: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to apply TRP or not."},
+    )
+    trp_depths: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "TRP depth value."
+        },
+    )
+    trp_p: Optional[float] = field(
+        default=0.1,
+        metadata={
+            "help": "TRP p value."
+        },
+    )
+    trp_lambdas: Optional[List[float]] = field(
+        default_factory=lambda: [0.4, 0.2, 0.1],
+        metadata={
+            "help": "TRP lambda values (list of floats)."
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    source_lang: str = field(default=None, metadata={"help": "Source language id for translation."})
+    target_lang: str = field(default=None, metadata={"help": "Target language id for translation."})
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a jsonlines)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "An optional input evaluation data file to evaluate the metrics (sacrebleu) on a jsonlines file."
+        },
+    )
+    test_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input test data file to evaluate the metrics (sacrebleu) on a jsonlines file."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_source_length: Optional[int] = field(
+        default=1024,
+        metadata={
+            "help": (
+                "The maximum total input sequence length after tokenization. Sequences longer "
+                "than this will be truncated, sequences shorter will be padded."
+            )
+        },
+    )
+    max_target_length: Optional[int] = field(
+        default=128,
+        metadata={
+            "help": (
+                "The maximum total sequence length for target text after tokenization. Sequences longer "
+                "than this will be truncated, sequences shorter will be padded."
+            )
+        },
+    )
+    val_max_target_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The maximum total sequence length for validation target text after tokenization. Sequences longer "
+                "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`. "
+                "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
+                "during ``evaluate`` and ``predict``."
+            )
+        },
+    )
+    pad_to_max_length: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to pad all samples to model maximum sentence length. "
+                "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
+                "efficient on GPU but very bad for TPU."
+            )
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+                "value if set."
+            )
+        },
+    )
+    num_beams: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": (
+                "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
+                "which is used during ``evaluate`` and ``predict``."
+            )
+        },
+    )
+    ignore_pad_token_for_loss: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
+        },
+    )
+    source_prefix: Optional[str] = field(
+        default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
+    )
+    forced_bos_token: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to force as the first generated token after the :obj:`decoder_start_token_id`.Useful for"
+                " multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token needs to"
+                " be the target language token.(Usually it is the target language token)"
+            )
+        },
+    )
+
+    def __post_init__(self):
+        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
+            raise ValueError("Need either a dataset name or a training/validation file.")
+        elif self.source_lang is None or self.target_lang is None:
+            raise ValueError("Need to specify the source language and the target language.")
+
+        # accepting both json and jsonl file extensions, as
+        # many jsonlines files actually have a .json extension
+        valid_extensions = ["json", "jsonl"]
+
+        if self.train_file is not None:
+            extension = self.train_file.split(".")[-1]
+            assert extension in valid_extensions, "`train_file` should be a jsonlines file."
+        if self.validation_file is not None:
+            extension = self.validation_file.split(".")[-1]
+            assert extension in valid_extensions, "`validation_file` should be a jsonlines file."
+        if self.val_max_target_length is None:
+            self.val_max_target_length = self.max_target_length
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_translation", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    if data_args.source_prefix is None and model_args.model_name_or_path in [
+        "google-t5/t5-small",
+        "google-t5/t5-base",
+        "google-t5/t5-large",
+        "google-t5/t5-3b",
+        "google-t5/t5-11b",
+    ]:
+        logger.warning(
+            "You're running a t5 model but didn't provide a source prefix, which is expected, e.g. with "
+            "`--source_prefix 'translate English to German: ' `"
+        )
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own JSON training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For translation, only JSON files are supported, with one field named "translation" containing two keys for the
+    # source and target languages (unless you adapt what follows).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
+        )
+    else:
+        data_files = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+            extension = data_args.validation_file.split(".")[-1]
+        if data_args.test_file is not None:
+            data_files["test"] = data_args.test_file
+            extension = data_args.test_file.split(".")[-1]
+        if extension == "jsonl":
+            builder_name = "json"  # the "json" builder reads both .json and .jsonl files
+        else:
+            builder_name = extension  # e.g. "parquet"
+        raw_datasets = load_dataset(
+            builder_name,
+            data_files=data_files,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+        )
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading.
+
+    # Load pretrained model and tokenizer
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    model = AutoModelForSeq2SeqLM.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    if model_args.apply_trp:
+        model = apply_trp(model, model_args.trp_depths, model_args.trp_p, model_args.trp_lambdas)
+
+    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
+    # on a small vocab and want a smaller embedding size, remove this test.
+    embedding_size = model.get_input_embeddings().weight.shape[0]
+    if len(tokenizer) > embedding_size:
+        model.resize_token_embeddings(len(tokenizer))
+
+    # Set decoder_start_token_id
+    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
+        if isinstance(tokenizer, MBartTokenizer):
+            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang]
+        else:
+            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang)
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
+
+    # Preprocessing the datasets.
+    # We need to tokenize inputs and targets.
+    if training_args.do_train:
+        column_names = raw_datasets["train"].column_names
+    elif training_args.do_eval:
+        column_names = raw_datasets["validation"].column_names
+    elif training_args.do_predict:
+        column_names = raw_datasets["test"].column_names
+    else:
+        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
+        return
+
+    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
+    # ignore those attributes).
+    if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
+        assert data_args.target_lang is not None and data_args.source_lang is not None, (
+            f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and "
+            "--target_lang arguments."
+        )
+
+        tokenizer.src_lang = data_args.source_lang
+        tokenizer.tgt_lang = data_args.target_lang
+
+        # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
+        # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
+        forced_bos_token_id = (
+            tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None
+        )
+        model.config.forced_bos_token_id = forced_bos_token_id
+
+    # Get the language codes for input/target.
+    source_lang = data_args.source_lang.split("_")[0]
+    target_lang = data_args.target_lang.split("_")[0]
+
+    # Check the whether the source target length fits in the model, if it has absolute positional embeddings
+    if (
+        hasattr(model.config, "max_position_embeddings")
+        and not hasattr(model.config, "relative_attention_max_distance")
+        and model.config.max_position_embeddings < data_args.max_source_length
+    ):
+        raise ValueError(
+            f"`--max_source_length` is set to {data_args.max_source_length}, but the model only has"
+            f" {model.config.max_position_embeddings} position encodings. Consider either reducing"
+            f" `--max_source_length` to {model.config.max_position_embeddings} or using a model with larger position "
+            "embeddings"
+        )
+
+    # Temporarily set max_target_length for training.
+    max_target_length = data_args.max_target_length
+    padding = "max_length" if data_args.pad_to_max_length else False
+
+    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
+        logger.warning(
+            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for "
+            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
+        )
+
+    def preprocess_function(examples):
+        inputs = [ex[source_lang] for ex in examples["translation"]]
+        targets = [ex[target_lang] for ex in examples["translation"]]
+        inputs = [prefix + inp for inp in inputs]
+        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
+
+        # Tokenize targets with the `text_target` keyword argument
+        labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)
+
+        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
+        # padding in the loss.
+        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
+            labels["input_ids"] = [
+                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
+            ]
+
+        model_inputs["labels"] = labels["input_ids"]
+        return model_inputs
+
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+        with training_args.main_process_first(desc="train dataset map pre-processing"):
+            train_dataset = train_dataset.map(
+                preprocess_function,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on train dataset",
+            )
+
+    if training_args.do_eval:
+        max_target_length = data_args.val_max_target_length
+        if "validation" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = raw_datasets["validation"]
+        if data_args.max_eval_samples is not None:
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+        with training_args.main_process_first(desc="validation dataset map pre-processing"):
+            eval_dataset = eval_dataset.map(
+                preprocess_function,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on validation dataset",
+            )
+
+    if training_args.do_predict:
+        max_target_length = data_args.val_max_target_length
+        if "test" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_dataset = raw_datasets["test"]
+        if data_args.max_predict_samples is not None:
+            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
+            predict_dataset = predict_dataset.select(range(max_predict_samples))
+        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
+            predict_dataset = predict_dataset.map(
+                preprocess_function,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on prediction dataset",
+            )
+
+    # Data collator
+    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
+    if data_args.pad_to_max_length:
+        data_collator = default_data_collator
+    else:
+        data_collator = DataCollatorForSeq2Seq(
+            tokenizer,
+            model=model,
+            label_pad_token_id=label_pad_token_id,
+            pad_to_multiple_of=8 if training_args.fp16 else None,
+        )
+
+    # Metric
+    metric = evaluate.load("sacrebleu", cache_dir=model_args.cache_dir)
+
+    def postprocess_text(preds, labels):
+        preds = [pred.strip() for pred in preds]
+        labels = [[label.strip()] for label in labels]
+
+        return preds, labels
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        if isinstance(preds, tuple):
+            preds = preds[0]
+        # Replace -100s used for padding as we can't decode them
+        preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
+        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        # Some simple post-processing
+        decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        result = {"bleu": result["score"]}
+
+        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
+        result["gen_len"] = np.mean(prediction_lens)
+        result = {k: round(v, 4) for k, v in result.items()}
+        return result
+
+    # Initialize our Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        processing_class=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    results = {}
+    max_length = (
+        training_args.generation_max_length
+        if training_args.generation_max_length is not None
+        else data_args.val_max_target_length
+    )
+    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix="eval")
+        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+
+        predict_results = trainer.predict(
+            predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams
+        )
+        metrics = predict_results.metrics
+        max_predict_samples = (
+            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
+        )
+        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
+
+        trainer.log_metrics("predict", metrics)
+        trainer.save_metrics("predict", metrics)
+
+        if trainer.is_world_process_zero():
+            if training_args.predict_with_generate:
+                predictions = predict_results.predictions
+                predictions = np.where(predictions != -100, predictions, tokenizer.pad_token_id)
+                predictions = tokenizer.batch_decode(
+                    predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
+                )
+                predictions = [pred.strip() for pred in predictions]
+                output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
+                with open(output_prediction_file, "w", encoding="utf-8") as writer:
+                    writer.write("\n".join(predictions))
+
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+
+    languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None]
+    if len(languages) > 0:
+        kwargs["language"] = languages
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hpo-examples/translation/trplib.py b/hpo-examples/translation/trplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0fb33879b1ab05f53e0b8e7068729abbb96146
--- /dev/null
+++ b/hpo-examples/translation/trplib.py
@@ -0,0 +1,1181 @@
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from torchvision.models.mobilenetv2 import MobileNetV2
+from torchvision.models.resnet import ResNet
+from torchvision.models.efficientnet import EfficientNet
+from torchvision.models.vision_transformer import VisionTransformer
+from torchvision.models.segmentation.fcn import FCN
+from torchvision.models.segmentation.deeplabv3 import DeepLabV3
+
+import transformers
+from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, CausalLMOutput, Seq2SeqLMOutput
+
+from typing import Optional, Tuple, List, Union, Callable
+from collections import OrderedDict
+import types
+
+
+def trp_criterion(trp_blocks: nn.ModuleList, shared_head: Callable, criterion: Callable, lambdas: List[float], hidden_states: Tensor, logits: Tensor, targets: Tensor, loss_normalization=False):
+    loss, mask = criterion(logits, targets)
+    if loss_normalization:
+        coeff = loss.detach()
+
+    embeds = [hidden_states]
+    predictions = []
+    for k, c in enumerate(lambdas):
+        embeds.append(trp_blocks[k](embeds[-1]))
+        predictions.append(shared_head(embeds[-1]))
+        replica_loss, mask = criterion(predictions[-1], targets, mask)
+        loss += c * replica_loss
+    
+    if loss_normalization:
+        with torch.no_grad():
+            coeff = torch.exp(coeff) / torch.exp(loss.detach())
+        loss = coeff * loss
+    
+    return loss
+
+
+class TPBlock(nn.Module):
+    def __init__(self, depths: int, in_features: int, p: float, dim=-1):
+        super(TPBlock, self).__init__()
+
+        self.dropout = nn.Dropout(p)
+
+        self.cdim = dim
+
+        blocks = []
+        for _ in range(depths):
+            blocks.append(nn.Linear(in_features, in_features))
+            nn.init.constant_(blocks[-1].weight, 0.0)
+            nn.init.constant_(blocks[-1].bias, 0.0)
+            blocks.append(nn.ReLU())
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        x = self.dropout(x)
+        if self.cdim == -1:
+            x = x + self.blocks(x)
+        else:
+            x = x + torch.movedim(self.blocks(torch.movedim(x, self.cdim, -1)), -1, self.cdim)
+        return x
+    
+
+class Config:
+    @staticmethod
+    def gen_criterion(*args, **kwargs):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor.
+                target (Tensor): Target labels.
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor with the same shape of target.
+            """
+            pass
+        return func
+    
+    @staticmethod
+    def gen_shared_head(*args, **kwargs):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States tensor.
+
+            Returns:
+                logits (Tensor): Logits tensor.
+            """
+            pass
+        return func
+
+    @staticmethod
+    def forward(*args, **kwargs):
+        pass
+
+
+# Wav2Vec2 for Audio Classification
+class Wav2Vec2ForSequenceClassificationConfig(Config): 
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self, attention_mask):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+            output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            if self.config.use_weighted_layer_sum:
+                hidden_states = outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION]
+                hidden_states = torch.stack(hidden_states, dim=1)
+                norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+                hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+            else:
+                hidden_states = outputs[0]
+
+            _hidden_states = self.projector(hidden_states)
+            if attention_mask is None:
+                pooled_output = _hidden_states.mean(dim=1)
+            else:
+                padding_mask = self._get_feature_vector_attention_mask(_hidden_states.shape[1], attention_mask)
+                expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, _hidden_states.shape[2])
+                _hidden_states[~expand_padding_mask] = 0.0
+                pooled_output = _hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                shared_head = Wav2Vec2ForSequenceClassificationConfig.gen_shared_head(self, attention_mask)
+                criterion = Wav2Vec2ForSequenceClassificationConfig.gen_criterion()
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states, logits.view(-1, self.config.num_labels),  labels.view(-1), loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForSequenceClassificationConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# MobileNetV2 for Image Classification
+class MobileNetV2Config(Config):
+    @staticmethod
+    def gen_criterion(label_smoothing=0.0, top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B] or [B, C].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            label = torch.argmax(target, dim=1) if label_smoothing > 0.0 else target
+                
+            unmasked_loss = F.cross_entropy(input, label, reduction="none", label_smoothing=label_smoothing)
+            if mask is None:
+                mask = torch.ones_like(unmasked_loss, dtype=torch.float32, device=target.device)
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=-1)
+                mask = mask * torch.eq(topk_indices, label[:, None]).any(dim=-1).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = MobileNetV2Config.gen_shared_head(self)
+                criterion = MobileNetV2Config.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ResNet for Image Classification
+class ResNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.fc(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.conv1(images)
+            x = self.bn1(x)
+            x = self.relu(x)
+            x = self.maxpool(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.fc(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = ResNetConfig.gen_shared_head(self)
+                criterion = ResNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# EfficientNet for Image Classification
+class EfficientNetConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self.features(images)
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            logits = self.classifier(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = EfficientNetConfig.gen_shared_head(self)
+                criterion = EfficientNetConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# ViT for Image Classification
+class VisionTransformerConfig(MobileNetV2Config):
+    @staticmethod
+    def gen_shared_head(self):
+        def func(x):
+            """
+            Args:
+                x (Tensor): Hidden States tensor of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.heads(x)
+            return logits
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, label_smoothing=0.0, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            x = self._process_input(images)
+            n = x.shape[0]
+            batch_class_token = self.class_token.expand(n, -1, -1)
+            x = torch.cat([batch_class_token, x], dim=1)
+            x = self.encoder(x)
+            x = x[:, 0]
+
+            logits = self.heads(x)
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                shared_head = VisionTransformerConfig.gen_shared_head(self)
+                criterion = VisionTransformerConfig.gen_criterion(label_smoothing, top_k)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, x, logits, targets, loss_normalization)
+                return logits, loss
+            return logits
+        return func
+
+
+# Bert for Question Answering
+class BertForQuestionAnsweringConfig(Config): 
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target: List[Tensor], mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C, 2].
+                target (List[Tensor]): 
+                    Start Positions of shape [B].
+                    End Positions of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            start_positions, end_positions = target
+
+            if mask is None:
+                mask = torch.ones_like(start_positions, dtype=torch.float32, device=start_positions.device)
+
+            start_logits, end_logits = input.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            masked_start_losses = F.cross_entropy(start_logits, start_positions, ignore_index=ignored_index, reduction="none")
+            start_loss = torch.sum(mask * masked_start_losses) / (torch.sum(mask) + 1e-6)
+            masked_end_losses = F.cross_entropy(end_logits, end_positions, ignore_index=ignored_index, reduction="none")
+            end_loss = torch.sum(mask * masked_end_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(start_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, start_positions[:, None]).any(dim=1).to(start_logits.dtype)
+                topk_values, topk_indices = torch.topk(end_logits, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, end_positions[:, None]).any(dim=1).to(end_logits.dtype)
+
+            return (start_loss + end_loss) / 2, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.qa_outputs(hidden_states)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            start_positions: Optional[torch.Tensor] = None,
+            end_positions: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+            r"""
+            start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the start of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for position (index) of the end of the labelled span for computing the token classification loss.
+                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+                are not taken into account for computing the loss.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            sequence_output = outputs[0]
+
+            logits = self.qa_outputs(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                shared_head = BertForQuestionAnsweringConfig.gen_shared_head(self)
+                criterion = BertForQuestionAnsweringConfig.gen_criterion()
+                total_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, [start_positions, end_positions], loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+    
+
+# FCN for Semantic Segmentation
+class FCNConfig(Config):
+    @staticmethod
+    def gen_criterion(top_k=1):
+        def func(input, target, mask=None):
+            """
+            Args:
+                input Tensor: input tensor of shape [B, C, H, W].
+                target (Tensor): Target labels of shape [B, H, W].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B, H, W].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+                
+            masked_loss = F.cross_entropy(input, target, ignore_index=255, reduction="none")
+            loss = torch.sum(mask * masked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(input, top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, target[:, None, :, :]).any(dim=1).to(input.dtype)
+                # mask = mask * torch.eq(torch.argmax(x, dim=1), target).to(x.dtype)
+                
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_out_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+    
+    @staticmethod
+    def gen_aux_shared_head(self, input_shape):
+        def func(features):
+            """
+            Args:
+                features (Tensor): features tensor of shape [B, hidden_units, H, W].
+
+            Returns:
+                result (Tensors): result tensor of shape [B, C, H, W].
+            """
+            x = self.aux_classifier(features)
+            result = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            return result
+        return func
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=True, top_k=1):
+        def func(self, images: Tensor, targets=None):
+            input_shape = images.shape[-2:]
+            # contract: features is a dict of tensors
+            features = self.backbone(images)
+
+            result = OrderedDict()
+            x = features["out"]
+            x = self.classifier(x)
+            x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+            result["out"] = x
+
+            if self.aux_classifier is not None:
+                x = features["aux"]
+                x = self.aux_classifier(x)
+                x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False)
+                result["aux"] = x
+
+            if self.training:
+                torch._assert(targets is not None, "targets should not be none when in training mode")
+                out_shared_head = FCNConfig.gen_out_shared_head(self, input_shape)
+                aux_shared_head = FCNConfig.gen_aux_shared_head(self, input_shape)
+                criterion = FCNConfig.gen_criterion(top_k)
+                out_loss = trp_criterion(self.out_trp_blocks, out_shared_head, criterion, lambdas, features["out"], result["out"], targets, loss_normalization)
+                aux_loss = trp_criterion(self.aux_trp_blocks, aux_shared_head, criterion, lambdas, features["aux"], result["aux"], targets, loss_normalization)
+                loss = out_loss + 0.5 * aux_loss
+                return result, loss
+            return result
+        return func
+
+
+# DeepLabV3Config for Semantic Segmentation
+class DeepLabV3Config(FCNConfig):
+    pass
+
+
+# Bert for Text Classification
+class BertForSequenceClassificationConfig(Config): 
+    @staticmethod
+    def gen_criterion():
+        def func(input, target, mask=None):
+            """
+            Args:
+                input (Tensor): Input tensor of shape [B, C].
+                target (Tensor): Target labels of shape [B].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(target, dtype=torch.float32, device=target.device)
+
+            unmasked_loss = F.cross_entropy(input, target, reduction="none")
+            loss = torch.sum(mask * unmasked_loss) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                mask = mask * torch.eq(torch.argmax(input, dim=1), target).to(input.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            token_type_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.bert(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            logits = self.classifier(pooled_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, pooled_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Boberta for Text Classification
+class RobertaForSequenceClassificationConfig(BertForSequenceClassificationConfig): 
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C].
+            """
+            logits = self.classifier(hidden_states)
+            return logits
+        return func
+        
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+                Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+                config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+                `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            outputs = self.roberta(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            sequence_output = outputs[0]
+            logits = self.classifier(sequence_output)
+
+            loss = None
+            if labels is not None:
+                assert self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int)  # TODO: remove this 
+                # move labels to correct device to enable model parallelism
+                labels = labels.to(logits.device)
+                if self.config.problem_type is None:
+                    if self.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    if self.num_labels == 1:
+                        loss = F.mse_loss(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = F.mse_loss(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    shared_head = BertForSequenceClassificationConfig.gen_shared_head(self)
+                    criterion = BertForSequenceClassificationConfig.gen_criterion()
+                    loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, sequence_output,  logits, labels, loss_normalization)
+                elif self.config.problem_type == "multi_label_classification":
+                    loss = F.binary_cross_entropy_with_logits(logits, labels)
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        return func
+
+
+# Wav2Vec2 for Speech Recognition
+class Wav2Vec2ForCTCConfig(Config):
+    _HIDDEN_STATES_START_POSITION = 2
+
+    @staticmethod
+    def greedy_decode_ctc(
+        log_probs: torch.Tensor,
+        input_lengths: torch.Tensor,
+        blank_token_id: int,
+        target_lengths: torch.Tensor
+    ):
+        """
+        Convert logits to flattened predictions that match the shape of flattened_targets.
+
+        Args:
+            log_probs: [B, L, V] - log-softmax output
+            input_lengths: [B] - actual length of each input
+            blank_token_id: int - index of blank token
+            target_lengths: [B] - used to determine how many predictions to keep per sample
+
+        Returns:
+            flattened_predictions: 1D tensor, same total length as sum(target_lengths)
+        """
+        batch_size = log_probs.size(0)
+        decoded_all = []
+
+        predicted_ids = log_probs.argmax(dim=-1)  # [B, L]
+
+        for i in range(batch_size):
+            pred = predicted_ids[i][:input_lengths[i]]  # [Li]
+            prev = None
+            decoded = []
+            for token in pred:
+                token = token.item()
+                if token != blank_token_id and token != prev:
+                    decoded.append(token)
+                prev = token
+            # Trim or pad to match target_lengths[i]
+            tgt_len = target_lengths[i].item()
+            if len(decoded) >= tgt_len:
+                decoded = decoded[:tgt_len]
+            else:
+                decoded = decoded + [blank_token_id] * (tgt_len - len(decoded))  # pad with blank
+            decoded_all.extend(decoded)
+
+        return torch.tensor(decoded_all, dtype=torch.long, device=log_probs.device)  # shape: [sum(target_lengths)]
+
+    @staticmethod
+    def gen_criterion(input_lengths: Tensor, pad_token_id: int, ctc_zero_infinity: bool):
+        def func(logits: Tensor, labels: Tensor, mask=None):
+            """
+            Args:
+                logits (Tensor): Log Probablities of shape [B, L, V].
+                labels (Tensor): Flattened Targets of shape [B, L'].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(input_lengths, dtype=torch.float32, device=input_lengths.device)
+
+            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            with torch.backends.cudnn.flags(enabled=False):
+                masked_losses = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=pad_token_id, reduction="none", zero_infinity=ctc_zero_infinity)
+                loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                thres = 0.5
+                flattened_predictions = Wav2Vec2ForCTCConfig.greedy_decode_ctc(
+                    log_probs.transpose(0, 1),  # [B, T, V]
+                    input_lengths=input_lengths,
+                    blank_token_id=pad_token_id,
+                    target_lengths=target_lengths
+                )
+                token_wise_mask =  torch.eq(flattened_predictions, flattened_targets).to(flattened_targets.dtype)
+                segment_ids = torch.arange(len(target_lengths), device=target_lengths.device).repeat_interleave(target_lengths)
+                sequence_wise_mask = torch.zeros(len(target_lengths), dtype=target_lengths.dtype, device=token_wise_mask.device).scatter_add(0, segment_ids, token_wise_mask)
+                mask = mask * torch.ge(sequence_wise_mask, thres * target_lengths).to(flattened_targets.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, C, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, C, 2].
+            """
+            logits = self.lm_head(hidden_states)
+            # log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
+            return logits
+        return func
+    
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_values: Optional[torch.Tensor],
+            attention_mask: Optional[torch.Tensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            labels: Optional[torch.Tensor] = None,
+        ) -> Union[Tuple, CausalLMOutput]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
+                Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
+                the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
+                All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
+                config.vocab_size - 1]`.
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None and labels.max() >= self.config.vocab_size:
+                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
+
+            outputs = self.wav2vec2(
+                input_values,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            hidden_states = self.dropout(hidden_states)
+
+            logits = self.lm_head(hidden_states)
+
+            loss = None
+            if labels is not None:
+                # retrieve loss input_lengths from attention_mask
+                attention_mask = (
+                    attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
+                )
+                input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+                shared_head = Wav2Vec2ForCTCConfig.gen_shared_head(self)
+                criterion = Wav2Vec2ForCTCConfig.gen_criterion(input_lengths, self.config.pad_token_id, self.config.ctc_zero_infinity)
+                loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, hidden_states,  logits, labels, loss_normalization)  # NOTE: Apply TRP!
+
+            if not return_dict:
+                output = (logits,) + outputs[Wav2Vec2ForCTCConfig._HIDDEN_STATES_START_POSITION:]
+                return ((loss,) + output) if loss is not None else output
+
+            return CausalLMOutput(
+                loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+            )
+        return func
+
+
+# MBart for Translation
+class MBartForConditionalGenerationConfig(Config): 
+    @staticmethod
+    def gen_criterion(vocab_size: int, top_k=1):
+        def func(logits, labels, mask=None):
+            """
+            Args:
+                logits (Tensor): Logits tensor of shape [B, L, V].
+                labels (Tensor): Target labels of shape [B, L].
+
+            Returns:
+                loss (Tensor): Scalar tensor representing the loss.
+                mask (Tensor): Boolean mask tensor of shape [B].
+            """
+            if mask is None:
+                mask = torch.ones_like(labels.view(-1), dtype=torch.float32, device=labels.device)
+                
+            masked_losses = F.cross_entropy(logits.view(-1, vocab_size), labels.view(-1), reduction="none")
+            loss = torch.sum(mask * masked_losses) / (torch.sum(mask) + 1e-6)
+
+            with torch.no_grad():
+                topk_values, topk_indices = torch.topk(logits.view(-1, vocab_size), top_k, dim=1)
+                mask = mask * torch.eq(topk_indices, labels.view(-1, 1)).any(dim=1).to(logits.dtype)
+
+            return loss, mask
+        return func
+    
+    @staticmethod
+    def gen_shared_head(self):
+        def func(hidden_states):
+            """
+            Args:
+                hidden_states (Tensor): Hidden States of shape [B, L, hidden_units].
+
+            Returns:
+                logits (Tensor): Logits tensor of shape [B, L].
+            """
+            logits = self.lm_head(hidden_states) + self.final_logits_bias
+            return logits
+        return func
+    
+    @staticmethod
+    def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+        """
+        Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
+        have a single `decoder_start_token_id` in contrast to other Bart-like models.
+        """
+        prev_output_tokens = input_ids.clone()
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+        index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+        decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+        prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+        prev_output_tokens[:, 0] = decoder_start_tokens
+
+        return prev_output_tokens
+
+    @staticmethod
+    def gen_forward(lambdas, loss_normalization=False):
+        def func(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.Tensor] = None,
+            decoder_head_mask: Optional[torch.Tensor] = None,
+            cross_attn_head_mask: Optional[torch.Tensor] = None,
+            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+        ) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
+            r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            Returns:
+
+            """
+            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+            if labels is not None:
+                # if use_cache:
+                #     logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+                use_cache = False
+                if decoder_input_ids is None and decoder_inputs_embeds is None:
+                    decoder_input_ids = MBartForConditionalGenerationConfig.shift_tokens_right(labels, self.config.pad_token_id)
+
+            outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=decoder_input_ids,
+                encoder_outputs=encoder_outputs,
+                decoder_attention_mask=decoder_attention_mask,
+                head_mask=head_mask,
+                decoder_head_mask=decoder_head_mask,
+                cross_attn_head_mask=cross_attn_head_mask,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+            masked_lm_loss = None
+            if labels is not None:
+                shared_head = MBartForConditionalGenerationConfig.gen_shared_head(self)
+                criterion = MBartForConditionalGenerationConfig.gen_criterion(self.config.vocab_size)
+                masked_lm_loss = trp_criterion(self.trp_blocks, shared_head, criterion, lambdas, outputs[0],  lm_logits, labels, loss_normalization)
+
+            if not return_dict:
+                output = (lm_logits,) + outputs[1:]
+                return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+            return Seq2SeqLMOutput(
+                loss=masked_lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                decoder_hidden_states=outputs.decoder_hidden_states,
+                decoder_attentions=outputs.decoder_attentions,
+                cross_attentions=outputs.cross_attentions,
+                encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+                encoder_hidden_states=outputs.encoder_hidden_states,
+                encoder_attentions=outputs.encoder_attentions,
+            )
+        return func
+
+
+def apply_trp(model, depths: int, p: float, lambdas: List[float], **kwargs):
+    if isinstance(model, transformers.Wav2Vec2ForSequenceClassification):
+        print("✅ Applying TRP to Wav2Vec2 for Audio Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, MobileNetV2):
+        print("✅ Applying TRP to MobileNetV2 for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(MobileNetV2Config.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, ResNet):
+        print("✅ Applying TRP to ResNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p) for _ in lambdas])
+        model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas, True, label_smoothing=0.0, top_k=1), model)
+    elif isinstance(model, EfficientNet):
+        print("✅ Applying TRP to EfficientNet for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1280, p) for _ in lambdas])
+        model.forward = types.MethodType(EfficientNetConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, VisionTransformer):
+        print("✅ Applying TRP to VisionTransformer for Image Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(VisionTransformerConfig.gen_forward(lambdas, True, label_smoothing=kwargs["label_smoothing"], top_k=1), model)
+    elif isinstance(model, transformers.BertForQuestionAnswering):
+        print("✅ Applying TRP to Bert for Question Answering...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForQuestionAnsweringConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, FCN):
+        print("✅ Applying TRP to FCN for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(FCNConfig.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, DeepLabV3):
+        print("✅ Applying TRP to DeepLabV3 for Semantic Segmentation...")
+        model.out_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 2048, p, dim=1) for _ in lambdas])
+        model.aux_trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p, dim=1) for _ in lambdas])
+        model.forward = types.MethodType(DeepLabV3Config.gen_forward(lambdas, True, 1), model)
+    elif isinstance(model, transformers.BertForSequenceClassification):
+        print("✅ Applying TRP to Bert for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(BertForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.RobertaForSequenceClassification):
+        print("✅ Applying TRP to Roberta for Text Classification...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 768, p) for _ in lambdas])
+        model.forward = types.MethodType(RobertaForSequenceClassificationConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.Wav2Vec2ForCTC):
+        print("✅ Applying TRP to Wav2Vec2 for Speech Recognition...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(Wav2Vec2ForCTCConfig.gen_forward(lambdas, False), model)
+    elif isinstance(model, transformers.MBartForConditionalGeneration):
+        print("✅ Applying TRP to MBart for Translation...")
+        model.trp_blocks = torch.nn.ModuleList([TPBlock(depths, 1024, p) for _ in lambdas])
+        model.forward = types.MethodType(MBartForConditionalGenerationConfig.gen_forward(lambdas, False), model)
+    else:
+        torch._assert(
+            isinstance(model, transformers.Wav2Vec2ForSequenceClassification), 
+            "The model should be an object of [`Wav2Vec2ForSequenceClassification`].")
+
+    return model
\ No newline at end of file