code: def main():
      parser = argparse.ArgumentParser(
          description="Simple example of training script tracking peak GPU memory usage."
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          default="bert-base-cased",
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          required=False,
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default=".",
          help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",
      )
      parser.add_argument(
          "--performance_lower_bound",
          type=float,
          default=None,
          help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.",
      )
      parser.add_argument(
          "--num_epochs",
          type=int,
          default=3,
          help="Number of train epochs.",
      )
      args = parser.parse_args()
      config = {
          "lr": 2e-5,
          "num_epochs": args.num_epochs,
          "seed": 42,
          "batch_size": 16,
      }
      training_function(config, args)
  
file_path: accelerate/src/accelerate/test_utils/scripts/external_deps/test_performance.py
imports: import argparse
  import json
  import os
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  from accelerate import Accelerator, DistributedType
  from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types