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.train_file is None
          and self.validation_file is None
          and self.test_file is None
      ):
          raise ValueError(
              "Need either a dataset name or a training/validation file/test_file."
          )
      else:
          if self.train_file is not None:
              extension = self.train_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
              ], "`train_file` should be a csv or a json file."
          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."
  
imports: import json
  import logging
  import os
  import sys
  import warnings
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Optional
  import evaluate
  import tensorflow
  from datasets import load_dataset
  from utils_qa import postprocess_qa_predictions
  import transformers
  from transformers import AutoConfig, AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizerFast, PushToHubCallback, TFAutoModelForQuestionAnswering, TFTrainingArguments, create_optimizer, set_seed
  from transformers.utils import CONFIG_NAME, TF2_WEIGHTS_NAME, check_min_version, send_example_telemetry
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