code: def main():
      # region Argument parsing
      # 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, TFTrainingArguments)
      )
      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, framework="tensorflow"
      )
  
      output_dir = Path(training_args.output_dir)
      output_dir.mkdir(parents=True, exist_ok=True)
      # endregion
  
      # region Checkpoints
      checkpoint = None
      if (
          len(os.listdir(training_args.output_dir)) > 0
          and not training_args.overwrite_output_dir
      ):
          if (output_dir / CONFIG_NAME).is_file() and (
              output_dir / TF2_WEIGHTS_NAME
          ).is_file():
              checkpoint = output_dir
              logger.info(
                  f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
                  " behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
              )
          else:
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                  "Use --overwrite_output_dir to continue regardless."
              )
      # endregion
  
      # region 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 training_args.should_log else logging.WARN
      )
  
      # Set the verbosity to info of the Transformers logger (on main process only):
      if training_args.should_log:
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
      logger.info(f"Training/evaluation parameters {training_args}")
      # endregion
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # region Load Data
      # 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.
          datasets = load_dataset(
              data_args.dataset_name,
              data_args.dataset_config_name,
              cache_dir=model_args.cache_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      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]
          datasets = load_dataset(
              extension,
              data_files=data_files,
              field="data",
              cache_dir=model_args.cache_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      # 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.html.
      # endregion
  
      # region 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,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      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,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      # endregion
  
      # region 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"
          )
      # endregion
  
      # region Preprocessing the datasets
      # Preprocessing is slightly different for training and evaluation.
      if training_args.do_train:
          column_names = datasets["train"].column_names
      elif training_args.do_eval:
          column_names = datasets["validation"].column_names
      else:
          column_names = 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
      )
  
      if data_args.pad_to_max_length or isinstance(
          training_args.strategy, tf.distribute.TPUStrategy
      ):
          logger.info(
              "Padding all batches to max length because argument was set or we're on TPU."
          )
          padding = "max_length"
      else:
          padding = False
  
      # 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=padding,
          )
  
          # 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]
              cls_index = input_ids.index(tokenizer.cls_token_id)
  
              # 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
  
      processed_datasets = dict()
      if training_args.do_train:
          if "train" not in datasets:
              raise ValueError("--do_train requires a train dataset")
          train_dataset = datasets["train"]
          if data_args.max_train_samples is not None:
              # We will select sample from whole data if agument 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
          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,
          )
          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)
              )
          processed_datasets["train"] = train_dataset
  
      # 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=padding,
          )
  
          # 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 datasets:
              raise ValueError(
                  "--do_eval requires a validation dataset"
              )
          eval_examples = 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
          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,
          )
          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)
              )
          processed_datasets["validation"] = eval_dataset
  
      if training_args.do_predict:
          if "test" not in datasets:
              raise ValueError("--do_predict requires a test dataset")
          predict_examples = 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
          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,
          )
          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)
              )
          processed_datasets["test"] = predict_dataset
      # endregion
  
      # region Metrics and 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,
              prefix=stage,
          )
          # Format the result to the format the metric expects.
          if data_args.version_2_with_negative:
              formatted_predictions = [
                  {
                      "id": k,
                      "prediction_text": v,
                      "no_answer_probability": 0.0,
                  }
                  for k, v in predictions.items()
              ]
          else:
              formatted_predictions = [
                  {"id": k, "prediction_text": v}
                  for k, v in predictions.items()
              ]
  
          references = [
              {"id": ex["id"], "answers": ex[answer_column_name]}
              for ex in examples
          ]
          return EvalPrediction(
              predictions=formatted_predictions, label_ids=references
          )
  
      metric = evaluate.load(
          "squad_v2" if data_args.version_2_with_negative else "squad"
      )
  
      def compute_metrics(p: EvalPrediction):
          return metric.compute(
              predictions=p.predictions, references=p.label_ids
          )
  
      # endregion
  
      with training_args.strategy.scope():
          dataset_options = tf.data.Options()
          dataset_options.experimental_distribute.auto_shard_policy = (
              tf.data.experimental.AutoShardPolicy.OFF
          )
          num_replicas = training_args.strategy.num_replicas_in_sync
  
          # region Load model and prepare datasets
          if checkpoint is None:
              model_path = model_args.model_name_or_path
          else:
              model_path = checkpoint
          model = TFAutoModelForQuestionAnswering.from_pretrained(
              model_path,
              config=config,
              cache_dir=model_args.cache_dir,
              revision=model_args.model_revision,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
          if training_args.do_train:
              training_dataset = model.prepare_tf_dataset(
                  processed_datasets["train"],
                  shuffle=True,
                  batch_size=training_args.per_device_train_batch_size
                  * num_replicas,
                  tokenizer=tokenizer,
              )
  
              training_dataset = training_dataset.with_options(
                  dataset_options
              )
  
              num_train_steps = (
                  len(training_dataset) * training_args.num_train_epochs
              )
              if training_args.warmup_steps > 0:
                  num_warmup_steps = training_args.warmup_steps
              elif training_args.warmup_ratio > 0:
                  num_warmup_steps = int(
                      num_train_steps * training_args.warmup_ratio
                  )
              else:
                  num_warmup_steps = 0
  
              optimizer, schedule = create_optimizer(
                  init_lr=training_args.learning_rate,
                  num_train_steps=len(training_dataset)
                  * training_args.num_train_epochs,
                  num_warmup_steps=num_warmup_steps,
                  adam_beta1=training_args.adam_beta1,
                  adam_beta2=training_args.adam_beta2,
                  adam_epsilon=training_args.adam_epsilon,
                  weight_decay_rate=training_args.weight_decay,
                  adam_global_clipnorm=training_args.max_grad_norm,
              )
  
              # no user-specified loss = will use the model internal loss
              model.compile(
                  optimizer=optimizer,
                  jit_compile=training_args.xla,
                  metrics=["accuracy"],
              )
  
          else:
              model.compile(
                  optimizer=None,
                  jit_compile=training_args.xla,
                  metrics=["accuracy"],
              )
              training_dataset = None
  
          if training_args.do_eval:
              eval_dataset = model.prepare_tf_dataset(
                  processed_datasets["validation"],
                  shuffle=False,
                  batch_size=training_args.per_device_train_batch_size
                  * num_replicas,
                  tokenizer=tokenizer,
              )
              eval_dataset = eval_dataset.with_options(dataset_options)
          else:
              eval_dataset = None
  
          if training_args.do_predict:
              predict_dataset = model.prepare_tf_dataset(
                  processed_datasets["test"],
                  shuffle=False,
                  batch_size=training_args.per_device_eval_batch_size
                  * num_replicas,
                  tokenizer=tokenizer,
              )
              predict_dataset = predict_dataset.with_options(
                  dataset_options
              )
          else:
              predict_dataset = None
  
          # endregion
  
          # region Preparing push_to_hub and model card
          push_to_hub_model_id = training_args.push_to_hub_model_id
          model_name = model_args.model_name_or_path.split("/")[-1]
          if not push_to_hub_model_id:
              if data_args.dataset_name is not None:
                  push_to_hub_model_id = (
                      f"{model_name}-finetuned-{data_args.dataset_name}"
                  )
              else:
                  push_to_hub_model_id = (
                      f"{model_name}-finetuned-question-answering"
                  )
  
          model_card_kwargs = {
              "finetuned_from": model_args.model_name_or_path,
              "tasks": "question-answering",
          }
          if data_args.dataset_name is not None:
              model_card_kwargs["dataset_tags"] = data_args.dataset_name
              if data_args.dataset_config_name is not None:
                  model_card_kwargs[
                      "dataset_args"
                  ] = data_args.dataset_config_name
                  model_card_kwargs[
                      "dataset"
                  ] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
              else:
                  model_card_kwargs["dataset"] = data_args.dataset_name
  
          if training_args.push_to_hub:
              callbacks = [
                  PushToHubCallback(
                      output_dir=training_args.output_dir,
                      model_id=push_to_hub_model_id,
                      organization=training_args.push_to_hub_organization,
                      token=training_args.push_to_hub_token,
                      tokenizer=tokenizer,
                      **model_card_kwargs,
                  )
              ]
          else:
              callbacks = []
          # endregion
  
          # region Training and Evaluation
  
          if training_args.do_train:
              # Note that the validation and test datasets have been processed in a different way to the
              # training datasets in this example, and so they don't have the same label structure.
              # As such, we don't pass them directly to Keras, but instead get model predictions to evaluate
              # after training.
              model.fit(
                  training_dataset,
                  epochs=int(training_args.num_train_epochs),
                  callbacks=callbacks,
              )
  
          if training_args.do_eval:
              logger.info("*** Evaluation ***")
  
              # In this example, we compute advanced metrics at the end of training, but
              # if you'd like to compute metrics every epoch that are too complex to be written as
              # standard Keras metrics, you can use our KerasMetricCallback. See
              # https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks
  
              eval_predictions = model.predict(eval_dataset)
              if isinstance(
                  eval_predictions.start_logits, tf.RaggedTensor
              ):
                  # If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea!
                  # The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even
                  # the highest probability in a sample. Instead, we use a large negative value, which ensures that the
                  # padding positions are correctly masked.
                  eval_start_logits = (
                      eval_predictions.start_logits.to_tensor(
                          default_value=-1000
                      ).numpy()
                  )
                  eval_end_logits = (
                      eval_predictions.end_logits.to_tensor(
                          default_value=-1000
                      ).numpy()
                  )
              else:
                  eval_start_logits = eval_predictions.start_logits
                  eval_end_logits = eval_predictions.end_logits
  
              post_processed_eval = post_processing_function(
                  datasets["validation"],
                  processed_datasets["validation"],
                  (eval_start_logits, eval_end_logits),
              )
              metrics = compute_metrics(post_processed_eval)
              logging.info("Evaluation metrics:")
              for metric, value in metrics.items():
                  logging.info(f"{metric}: {value:.3f}")
              if training_args.output_dir is not None:
                  output_eval_file = os.path.join(
                      training_args.output_dir, "all_results.json"
                  )
                  with open(output_eval_file, "w") as writer:
                      writer.write(json.dumps(metrics))
          # endregion
  
          # region Prediction
          if training_args.do_predict:
              logger.info("*** Predict ***")
  
              test_predictions = model.predict(predict_dataset)
              if isinstance(
                  test_predictions.start_logits, tf.RaggedTensor
              ):
                  # If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea!
                  # The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even
                  # the highest probability in a sample. Instead, we use a large negative value, which ensures that the
                  # padding positions are correctly masked.
                  test_start_logits = (
                      test_predictions.start_logits.to_tensor(
                          default_value=-1000
                      ).numpy()
                  )
                  test_end_logits = (
                      test_predictions.end_logits.to_tensor(
                          default_value=-1000
                      ).numpy()
                  )
              else:
                  test_start_logits = test_predictions.start_logits
                  test_end_logits = test_predictions.end_logits
              post_processed_test = post_processing_function(
                  datasets["test"],
                  processed_datasets["test"],
                  (test_start_logits, test_end_logits),
              )
              metrics = compute_metrics(post_processed_test)
  
              logging.info("Test metrics:")
              for metric, value in metrics.items():
                  logging.info(f"{metric}: {value:.3f}")
          # endregion
  
      if (
          training_args.output_dir is not None
          and not training_args.push_to_hub
      ):
          # If we're not pushing to hub, at least save a local copy when we're done
          model.save_pretrained(training_args.output_dir)
  
imports: import json
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Optional
  import tensorflow
  from datasets import load_dataset
  import evaluate
  import transformers
  from transformers import AutoConfig, AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizerFast, PushToHubCallback, TFAutoModelForQuestionAnswering, TFTrainingArguments, create_optimizer, set_seed
  from transformers.utils import CONFIG_NAME, TF2_WEIGHTS_NAME, check_min_version, send_example_telemetry
  from utils_qa import postprocess_qa_predictions
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: tokenized_examples, tokenized_examples, EvalPrediction, metric