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.
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 random
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  import numpy
  import torch
  from datasets import load_dataset
  from PIL import Image
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms import functional
  import evaluate
  import transformers
  from huggingface_hub import hf_hub_download
  from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForSemanticSegmentation, HfArgumentParser, Trainer, TrainingArguments, default_data_collator
  from transformers.trainer_utils import get_last_checkpoint
  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