code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a text classification task"
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to a pretrained model or model identifier from huggingface.co/models.",
          default="nvidia/mit-b0",
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          help="Name of the dataset on the hub.",
          default="segments/sidewalk-semantic",
      )
      parser.add_argument(
          "--reduce_labels",
          action="store_true",
          help="Whether or not to reduce all labels by 1 and replace background by 255.",
      )
      parser.add_argument(
          "--train_val_split",
          type=float,
          default=0.15,
          help="Fraction of the dataset to be used for validation.",
      )
      parser.add_argument(
          "--cache_dir",
          type=str,
          help="Path to a folder in which the model and dataset will be cached.",
      )
      parser.add_argument(
          "--use_auth_token",
          action="store_true",
          help="Whether to use an authentication token to access the model repository.",
      )
      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(
          "--adam_beta1",
          type=float,
          default=0.9,
          help="Beta1 for AdamW optimizer",
      )
      parser.add_argument(
          "--adam_beta2",
          type=float,
          default=0.999,
          help="Beta2 for AdamW optimizer",
      )
      parser.add_argument(
          "--adam_epsilon",
          type=float,
          default=1e-8,
          help="Epsilon for AdamW optimizer",
      )
      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="polynomial",
          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(
          "--trust_remote_code",
          type=bool,
          default=False,
          help=(
              "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
              "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
              "execute code present on the Hub on your local machine."
          ),
      )
      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",
          required=False,
          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"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
              "Only applicable when `--with_tracking` is passed."
          ),
      )
      args = parser.parse_args()
  
      # Sanity checks
      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 math
  import os
  import random
  from pathlib import Path
  import datasets
  import evaluate
  import numpy
  import torch
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from datasets import load_dataset
  from huggingface_hub import Repository, create_repo, hf_hub_download
  from PIL import Image
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.transforms import functional
  from tqdm.auto import tqdm
  import transformers
  from transformers import AutoConfig, AutoImageProcessor, AutoModelForSemanticSegmentation, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args