code: 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, TFTrainingArguments)
      )
      (
          model_args,
          data_args,
          training_args,
      ) = parser.parse_args_into_dataclasses()
  
      if (
          os.path.exists(training_args.output_dir)
          and os.listdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          raise ValueError(
              f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
              " --overwrite_output_dir to overcome."
          )
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(
          f"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, "
          f"16-bits training: {training_args.fp16}"
      )
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      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,
      )
  
      train_dataset, eval_dataset, test_ds, label2id = get_tfds(
          train_file=data_args.train_file,
          eval_file=data_args.dev_file,
          test_file=data_args.test_file,
          tokenizer=tokenizer,
          label_column_id=data_args.label_column_id,
          max_seq_length=data_args.max_seq_length,
      )
  
      config = AutoConfig.from_pretrained(
          model_args.config_name
          if model_args.config_name
          else model_args.model_name_or_path,
          num_labels=len(label2id),
          label2id=label2id,
          id2label={id: label for label, id in label2id.items()},
          finetuning_task="text-classification",
          cache_dir=model_args.cache_dir,
      )
  
      with training_args.strategy.scope():
          model = TFAutoModelForSequenceClassification.from_pretrained(
              model_args.model_name_or_path,
              from_pt=bool(".bin" in model_args.model_name_or_path),
              config=config,
              cache_dir=model_args.cache_dir,
          )
  
      def compute_metrics(p: EvalPrediction) -> Dict:
          preds = np.argmax(p.predictions, axis=1)
  
          return {"acc": (preds == p.label_ids).mean()}
  
      # Initialize our Trainer
      trainer = TFTrainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          compute_metrics=compute_metrics,
      )
  
      # Training
      if training_args.do_train:
          trainer.train()
          trainer.save_model()
          tokenizer.save_pretrained(training_args.output_dir)
  
      # Evaluation
      results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
          result = trainer.evaluate()
          output_eval_file = os.path.join(
              training_args.output_dir, "eval_results.txt"
          )
  
          with open(output_eval_file, "w") as writer:
              logger.info("***** Eval results *****")
  
              for key, value in result.items():
                  logger.info(f"  {key} = {value}")
                  writer.write(f"{key} = {value}\n")
  
              results.update(result)
  
      return results
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/legacy/text-classification/run_tf_text_classification.py
imports: import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, Optional
  import datasets
  import numpy
  import tensorflow
  from transformers import AutoConfig, AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizer, TFAutoModelForSequenceClassification, TFTrainer, TFTrainingArguments
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: results, dict