code: def parse_input_arguments(
      hparams: Dict[str, Any]
  ) -> Tuple[ModelArguments, DataTrainingArguments, TrainingArguments]:
      training_arguments = hparams.get("training_arguments", {})
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
      dataclass_types = (
          ModelArguments,
          DataTrainingArguments,
          TrainingArguments,
      )
      parser = HfArgumentParser(dataclass_types)
  
      # 1. If your arguments are defined in a file:
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          args_fname = os.path.abspath(sys.argv[1])
          args = json.loads(Path(args_fname).read_text())
          args = args.update(training_arguments)
          model_args, data_args, training_args = parser.parse_dict(args)
      # 2. If your arguments are in a sys.argv:
      else:
          args = sys.argv[1:]
          args.extend(dict2args(training_arguments))
          if any("--deepspeed" == arg.strip() for arg in args):
              args = dsat.get_hf_args_with_overwrites(args, hparams)
          (
              model_args,
              data_args,
              training_args,
          ) = parser.parse_args_into_dataclasses(
              args, look_for_args_file=False
          )
  
      return model_args, data_args, training_args
  
imports: import json
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Any, Dict, Optional, Tuple
  import datasets
  import numpy
  import torch
  import transformers
  from datasets import load_dataset
  from PIL import Image
  from torch.utils.tensorboard import SummaryWriter
  from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor
  from transformers import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoFeatureExtractor, AutoModelForImageClassification, HfArgumentParser, Trainer, TrainingArguments
  from transformers.integrations import TensorBoardCallback
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils.versions import require_version
  import determined
  from determined.pytorch import dsat
  from determined.transformers import DetCallback
label_desc: Use image node_id 4 for calling a global function with example usage: parse_input_arguments(hparams) and returns: model_args,  data_args,  training_args