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 random
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import evaluate
  import numpy
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms import functional
  import transformers
  from transformers import AutoConfig, AutoImageProcessor, 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