code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Fine-tune a Transformers model on an image classification dataset"
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default="cifar10",
          help=(
              "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
              " dataset)."
          ),
      )
      parser.add_argument(
          "--train_dir",
          type=str,
          default=None,
          help="A folder containing the training data.",
      )
      parser.add_argument(
          "--validation_dir",
          type=str,
          default=None,
          help="A folder containing the validation data.",
      )
      parser.add_argument(
          "--max_train_samples",
          type=int,
          default=None,
          help=(
              "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          ),
      )
      parser.add_argument(
          "--max_eval_samples",
          type=int,
          default=None,
          help=(
              "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
              "value if set."
          ),
      )
      parser.add_argument(
          "--train_val_split",
          type=float,
          default=0.15,
          help="Percent to split off of train for validation",
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          default="google/vit-base-patch16-224-in21k",
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--per_device_eval_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the evaluation dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--weight_decay",
          type=float,
          default=0.0,
          help="Weight decay to use.",
      )
      parser.add_argument(
          "--num_train_epochs",
          type=int,
          default=3,
          help="Total number of training epochs to perform.",
      )
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=None,
          help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      parser.add_argument(
          "--lr_scheduler_type",
          type=SchedulerType,
          default="linear",
          help="The scheduler type to use.",
          choices=[
              "linear",
              "cosine",
              "cosine_with_restarts",
              "polynomial",
              "constant",
              "constant_with_warmup",
          ],
      )
      parser.add_argument(
          "--num_warmup_steps",
          type=int,
          default=0,
          help="Number of steps for the warmup in the lr scheduler.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default=None,
          help="Where to store the final model.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=None,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the model to the Hub.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          help="The name of the repository to keep in sync with the local `output_dir`.",
      )
      parser.add_argument(
          "--hub_token",
          type=str,
          help="The token to use to push to the Model Hub.",
      )
      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 enable experiment trackers for logging.",
      )
      parser.add_argument(
          "--report_to",
          type=str,
          default="all",
          help=(
              'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
              ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
              "Only applicable when `--with_tracking` is passed."
          ),
      )
      parser.add_argument(
          "--ignore_mismatched_sizes",
          action="store_true",
          help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
      )
      args = parser.parse_args()
  
      # Sanity checks
      if (
          args.dataset_name is None
          and args.train_dir is None
          and args.validation_dir is None
      ):
          raise ValueError(
              "Need either a dataset name or a training/validation folder."
          )
  
      if args.push_to_hub or args.with_tracking:
          if args.output_dir is None:
              raise ValueError(
                  "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified."
              )
  
      if args.output_dir is not None:
          os.makedirs(args.output_dir, exist_ok=True)
  
      return args
  
imports: import argparse
  import json
  import logging
  import math
  import os
  from pathlib import Path
  import datasets
  import torch
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor
  from tqdm.auto import tqdm
  import evaluate
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from huggingface_hub import Repository
  from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForImageClassification, SchedulerType, get_scheduler
  from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
  from transformers.utils.versions import require_version
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args