code: def main():
      args = parse_args()
      train_dataloader, eval_dataloader = data_processing(
          args.num_samples, args.batch_size
      )
      model = AutoModelForSequenceClassification.from_pretrained(
          "bert-base-cased", num_labels=5
      )
      optimizer_cls = getattr(
          sys.modules["torch.optim"], args.optimizer
      )
      if (
          "capturable"
          in inspect.signature(optimizer_cls).parameters.keys()
      ):
          optimizer = optimizer_cls(
              model.parameters(), lr=args.lr, capturable=True
          )
      else:
          optimizer = optimizer_cls(model.parameters(), lr=args.lr)
      native_start = time.time()
      ref_loss, accuracy = model_training_evaluation(
          None,
          train_dataloader,
          eval_dataloader,
          model,
          optimizer,
          args.epochs,
          args.evaluation,
      )
      native_end = time.time()
      res_loss, accuracy = model_training_evaluation(
          args.backend,
          train_dataloader,
          eval_dataloader,
          model,
          optimizer,
          args.epochs,
          args.evaluation,
      )
      dynamo_end = time.time()
      if check_loss(ref_loss, res_loss):
          print(
              "[PASSED] TorchDynamo end to end training loss is less than or equal to native PyTorch"
          )
      else:
          print(
              "[FAILED] TorchDynamo end to end training loss is greater than native Pytorch"
          )
      if args.evaluation:
          print(f"Model accuracy: {accuracy}")
      native_elapsed = native_end - native_start
      dynamo_elapsed = dynamo_end - native_end
      print(
          f"Train model on {args.epochs} epochs with backend {args.backend} and optimizer {args.optimizer}:"
      )
      print(
          f"PyTorch spent {timedelta(seconds=native_elapsed/args.epochs)} per epoch"
      )
      print(
          f"TorchDynamo spent {timedelta(seconds=dynamo_elapsed/args.epochs)} per epoch"
      )
  
file_path: pytorch/benchmarks/dynamo/training_loss.py
imports: import argparse
  import inspect
  import os
  import sys
  import time
  from datetime import timedelta
  import torch
  import torch._dynamo
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types