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.",
      )
      parser.add_argument(
          "--cpu",
          action="store_true",
          help="If passed, will train on the CPU.",
      )
      parser.add_argument(
          "--checkpointing_steps",
          type=str,
          default=None,
          help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
      )
      parser.add_argument(
          "--resume_from_checkpoint",
          type=str,
          default=None,
          help="If the training should continue from a checkpoint folder.",
      )
      parser.add_argument(
          "--with_tracking",
          action="store_true",
          help="Whether to load in all available experiment trackers from the environment and use them for logging.",
      )
      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(
          "--logging_dir",
          type=str,
          default="logs",
          help="Location on where to store experiment tracking logs`",
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          required=True,
      )
      args = parser.parse_args()
      config = {
          "lr": 2e-5,
          "num_epochs": 3,
          "seed": 42,
          "batch_size": 16,
      }
      training_function(config, args)
  
imports: import argparse
  import gc
  import os
  import threading
  import evaluate
  import psutil
  import torch
  from datasets import load_dataset
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
  from accelerate.utils import is_npu_available, is_xpu_available
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types