code: def convert_examples_to_features(
      examples: List[InputExample],
      label_list: List[str],
      max_length: int,
      tokenizer: PreTrainedTokenizer,
  ) -> List[InputFeatures]:
      """
      Loads a data file into a list of `InputFeatures`
      """
  
      label_map = {label: i for i, label in enumerate(label_list)}
  
      features = []
      for ex_index, example in tqdm.tqdm(
          enumerate(examples), desc="convert examples to features"
      ):
          if ex_index % 10000 == 0:
              logger.info(
                  "Writing example %d of %d" % (ex_index, len(examples))
              )
          choices_inputs = []
          for ending_idx, (context, ending) in enumerate(
              zip(example.contexts, example.endings)
          ):
              text_a = context
              if example.question.find("_") != -1:
                  # this is for cloze question
                  text_b = example.question.replace("_", ending)
              else:
                  text_b = example.question + " " + ending
  
              inputs = tokenizer(
                  text_a,
                  text_b,
                  add_special_tokens=True,
                  max_length=max_length,
                  padding="max_length",
                  truncation=True,
                  return_overflowing_tokens=True,
              )
              if (
                  "num_truncated_tokens" in inputs
                  and inputs["num_truncated_tokens"] > 0
              ):
                  logger.info(
                      "Attention! you are cropping tokens (swag task is ok). "
                      "If you are training ARC and RACE and you are poping question + options, "
                      "you need to try to use a bigger max seq length!"
                  )
  
              choices_inputs.append(inputs)
  
          label = label_map[example.label]
  
          input_ids = [x["input_ids"] for x in choices_inputs]
          attention_mask = (
              [x["attention_mask"] for x in choices_inputs]
              if "attention_mask" in choices_inputs[0]
              else None
          )
          token_type_ids = (
              [x["token_type_ids"] for x in choices_inputs]
              if "token_type_ids" in choices_inputs[0]
              else None
          )
  
          features.append(
              InputFeatures(
                  example_id=example.example_id,
                  input_ids=input_ids,
                  attention_mask=attention_mask,
                  token_type_ids=token_type_ids,
                  label=label,
              )
          )
  
      for f in features[:2]:
          logger.info("*** Example ***")
          logger.info("feature: %s" % f)
  
      return features
  
imports: import csv
  import glob
  import json
  import logging
  import os
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional
  import tqdm
  from filelock import FileLock
  from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
label_desc: Use image node_id 1 for calling a global function with example usage: convert_examples_to_features(examples, label_list, max_length, tokenizer) and returns: features