class_docstr: Arguments pertaining to what data we are going to input our model for training and eval.
class_docstr_tok: ["Arguments","pertaining","to","what","data","we","are","going","to","input","our","model","for","training","and","eval","."]
code: def __post_init__(self):
      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."
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional, Union
  import datasets
  import numpy
  import torch
  from datasets import load_dataset
  import adapters
  import transformers
  from adapters import AdapterArguments, AdapterTrainer, setup_adapter_training
  from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import PaddingStrategy, check_min_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