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
      ):
          raise ValueError(
              "Need either a dataset name or a training/validation file."
          )
      else:
          if self.train_file is not None:
              extension = self.train_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
                  "txt",
              ], "`train_file` should be a csv, a json or a txt file."
          if self.validation_file is not None:
              extension = self.validation_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
                  "txt",
              ], "`validation_file` should be a csv, a json or a txt file."
  
imports: import json
  import logging
  import math
  import os
  import random
  import sys
  from dataclasses import dataclass, field
  from itertools import chain
  from pathlib import Path
  from typing import Optional
  import datasets
  import tensorflow
  from datasets import load_dataset
  from sklearn.model_selection import train_test_split
  import transformers
  from transformers import CONFIG_MAPPING, CONFIG_NAME, TF2_WEIGHTS_NAME, TF_MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoTokenizer, HfArgumentParser, PushToHubCallback, TFAutoModelForCausalLM, TFTrainingArguments, create_optimizer, set_seed
  from transformers.utils import 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