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
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import evaluate
  import numpy
  import tensorflow
  from datasets import load_dataset
  from PIL import Image
  import transformers
  from transformers import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoImageProcessor, DefaultDataCollator, HfArgumentParser, PushToHubCallback, TFAutoModelForImageClassification, TFTrainingArguments, create_optimizer, set_seed
  from transformers.keras_callbacks import KerasMetricCallback
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
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