code: def main():
      parser = argparse.ArgumentParser(
          description="Simple example of training script."
      )
      parser.add_argument(
          "--mixed_precision",
          type=str,
          default=None,
          choices=["no", "fp16", "bf16", "fp8"],
          help="Whether to use mixed precision. Choose"
          "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
          "and an Nvidia Ampere GPU.",
      )
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="The number of minibatches to be ran before gradients are accumulated.",
      )
      parser.add_argument(
          "--cpu",
          action="store_true",
          help="If passed, will train on the CPU.",
      )
      args = parser.parse_args()
      config = {
          "lr": 2e-5,
          "num_epochs": 3,
          "seed": 42,
          "batch_size": 16,
      }
      training_function(config, args)
  
file_path: accelerate/examples/by_feature/gradient_accumulation.py
imports: import argparse
  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
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types