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 json
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from functools import partial
  from itertools import chain
  from typing import Optional
  import datasets
  import numpy
  import transformers
  from datasets import load_dataset
  from onnxruntime.quantization import QuantFormat, QuantizationMode, QuantType
  from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from transformers.utils import check_min_version
  from optimum.onnxruntime import ORTModelForMultipleChoice, ORTQuantizer
  from optimum.onnxruntime.configuration import AutoCalibrationConfig, QuantizationConfig
  from optimum.onnxruntime.model import ORTModel
  from optimum.onnxruntime.preprocessors import QuantizationPreprocessor
  from optimum.onnxruntime.preprocessors.passes import ExcludeGeLUNodes, ExcludeLayerNormNodes, ExcludeNodeAfter, ExcludeNodeFollowedBy
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