class_docstr: Arguments pertaining to what data we are going to input our model for training and eval.
  Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify
  them on the command line.
class_docstr_tok: ["Arguments","pertaining","to","what","data","we","are","going","to","input","our","model","for","training","and","eval",".","Using","`","HfArgumentParser","`","we","can","turn","this","class","into","argparse","arguments","to","be","able","to","specify","them","on","the","command","line","."]
code: def __post_init__(self):
      if self.dataset_name is None and (
          self.train_dir is None and self.validation_dir is None
      ):
          raise ValueError(
              "You must specify either a dataset name from the hub or a train and/or validation directory."
          )
  
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 1 for calling the DataTrainingArguments obj's underlying member method code with example usage: obj.__post_init__() without return types