class_docstr: Arguments pertaining to what data we are going to input our model for training and eval.
code: def __post_init__(self):
      if (
          self.dataset_name is None
          and self.validation_file is None
          and self.test_file is None
      ):
          raise ValueError(
              "Need either a dataset name or a validation/test file."
          )
      else:
          if self.validation_file is not None:
              extension = self.validation_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
              ], "`validation_file` should be a csv or a json file."
          if self.test_file is not None:
              extension = self.test_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
              ], "`test_file` should be a csv or a json file."
      self.task_name = self.task_name.lower()
  
imports: import json
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from functools import partial
  from typing import Optional
  import datasets
  import numpy
  import transformers
  from datasets import ClassLabel, load_dataset
  from evaluate import load
  from transformers import AutoTokenizer, HfArgumentParser, PreTrainedTokenizer, TrainingArguments
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
  from optimum.onnxruntime import ORTModelForTokenClassification, ORTOptimizer
  from optimum.onnxruntime.configuration import OptimizationConfig
  from optimum.onnxruntime.model import ORTModel
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