code: def parse_args():
      parser = argparse.ArgumentParser(
          description="TorchDynamo end to end training/evaluation benchmark"
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=10,
          help="number of epochs to train (default: 10)",
      )
      parser.add_argument(
          "--num-samples",
          type=int,
          default=1000,
          help="number of samples to train/eval (default: 1000)",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=8,
          help="input batch size for training (default: 8)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=5e-5,
          help="learning rate (default: 5e-5)",
      )
      parser.add_argument(
          "--backend",
          choices=torch._dynamo.list_backends(exclude_tags=None),
          default="inductor",
          help="train/evaluate model with a given backend (default: inductor)",
      )
      parser.add_argument(
          "--optimizer",
          default="Adam",
          help="train model using a given optimizer (default: Adam)",
      )
      parser.add_argument(
          "--evaluation",
          action="store_true",
          help="running evaluation after model training",
      )
      args = parser.parse_args()
      return args
  
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 5 for calling a global function with example usage: parse_args() and returns: args