code: def convert_examples_to_features(
      examples: List[InputExample],
      label_list: List[str],
      max_seq_length: int,
      tokenizer: PreTrainedTokenizer,
      cls_token_at_end=False,
      cls_token="[CLS]",
      cls_token_segment_id=1,
      sep_token="[SEP]",
      sep_token_extra=False,
      pad_on_left=False,
      pad_token=0,
      pad_token_segment_id=0,
      pad_token_label_id=-100,
      sequence_a_segment_id=0,
      mask_padding_with_zero=True,
      logger=logging.getLogger(__name__),
  ) -> List[InputFeatures]:
      """Loads a data file into a list of `InputFeatures`
      `cls_token_at_end` define the location of the CLS token:
          - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
          - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
      `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
      """
  
      label_map = {label: i for i, label in enumerate(label_list)}
  
      features = []
      for ex_index, example in enumerate(examples):
          if ex_index % 10_000 == 0:
              logger.info(
                  "Writing example %d of %d", ex_index, len(examples)
              )
  
          tokens = []
          label_ids = []
          for word, label in zip(example.words, example.labels):
              word_tokens = tokenizer.tokenize(word)
  
              # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
              if len(word_tokens) > 0:
                  tokens.extend(word_tokens)
                  # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                  label_ids.extend(
                      [label_map[label]]
                      + [pad_token_label_id] * (len(word_tokens) - 1)
                  )
  
          # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
          special_tokens_count = tokenizer.num_special_tokens_to_add()
          if len(tokens) > max_seq_length - special_tokens_count:
              tokens = tokens[: (max_seq_length - special_tokens_count)]
              label_ids = label_ids[
                  : (max_seq_length - special_tokens_count)
              ]
  
          # The convention in BERT is:
          # (a) For sequence pairs:
          #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
          #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
          # (b) For single sequences:
          #  tokens:   [CLS] the dog is hairy . [SEP]
          #  type_ids:   0   0   0   0  0     0   0
          #
          # Where "type_ids" are used to indicate whether this is the first
          # sequence or the second sequence. The embedding vectors for `type=0` and
          # `type=1` were learned during pre-training and are added to the wordpiece
          # embedding vector (and position vector). This is not *strictly* necessary
          # since the [SEP] token unambiguously separates the sequences, but it makes
          # it easier for the model to learn the concept of sequences.
          #
          # For classification tasks, the first vector (corresponding to [CLS]) is
          # used as as the "sentence vector". Note that this only makes sense because
          # the entire model is fine-tuned.
          tokens += [sep_token]
          label_ids += [pad_token_label_id]
          if sep_token_extra:
              # roberta uses an extra separator b/w pairs of sentences
              tokens += [sep_token]
              label_ids += [pad_token_label_id]
          segment_ids = [sequence_a_segment_id] * len(tokens)
  
          if cls_token_at_end:
              tokens += [cls_token]
              label_ids += [pad_token_label_id]
              segment_ids += [cls_token_segment_id]
          else:
              tokens = [cls_token] + tokens
              label_ids = [pad_token_label_id] + label_ids
              segment_ids = [cls_token_segment_id] + segment_ids
  
          input_ids = tokenizer.convert_tokens_to_ids(tokens)
  
          # The mask has 1 for real tokens and 0 for padding tokens. Only real
          # tokens are attended to.
          input_mask = [1 if mask_padding_with_zero else 0] * len(
              input_ids
          )
  
          # Zero-pad up to the sequence length.
          padding_length = max_seq_length - len(input_ids)
          if pad_on_left:
              input_ids = ([pad_token] * padding_length) + input_ids
              input_mask = (
                  [0 if mask_padding_with_zero else 1] * padding_length
              ) + input_mask
              segment_ids = (
                  [pad_token_segment_id] * padding_length
              ) + segment_ids
              label_ids = (
                  [pad_token_label_id] * padding_length
              ) + label_ids
          else:
              input_ids += [pad_token] * padding_length
              input_mask += [
                  0 if mask_padding_with_zero else 1
              ] * padding_length
              segment_ids += [pad_token_segment_id] * padding_length
              label_ids += [pad_token_label_id] * padding_length
  
          assert len(input_ids) == max_seq_length
          assert len(input_mask) == max_seq_length
          assert len(segment_ids) == max_seq_length
          assert len(label_ids) == max_seq_length
  
          if ex_index < 5:
              logger.info("*** Example ***")
              logger.info("guid: %s", example.guid)
              logger.info(
                  "tokens: %s", " ".join([str(x) for x in tokens])
              )
              logger.info(
                  "input_ids: %s", " ".join([str(x) for x in input_ids])
              )
              logger.info(
                  "input_mask: %s",
                  " ".join([str(x) for x in input_mask]),
              )
              logger.info(
                  "segment_ids: %s",
                  " ".join([str(x) for x in segment_ids]),
              )
              logger.info(
                  "label_ids: %s", " ".join([str(x) for x in label_ids])
              )
  
          if "token_type_ids" not in tokenizer.model_input_names:
              segment_ids = None
  
          features.append(
              InputFeatures(
                  input_ids=input_ids,
                  attention_mask=input_mask,
                  token_type_ids=segment_ids,
                  label_ids=label_ids,
              )
          )
      return features
  
imports: import pandas
  import json
  import logging
  import os
  import torch
  from torch import nn
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  from pathlib import Path
  import pickle
  from filelock import FileLock
  import re
  import shutil
  from sklearn.model_selection import train_test_split
  from torch.utils.data import Dataset, TensorDataset, DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers import PreTrainedTokenizer, AutoTokenizer
label_desc: Use image node_id 2 for calling a global function with example usage: convert_examples_to_features(examples, label_list, max_seq_length, tokenizer, cls_token_at_end, cls_token, cls_token_segment_id, sep_token, sep_token_extra, pad_on_left, pad_token, pad_token_segment_id, pad_token_label_id, sequence_a_segment_id, mask_padding_with_zero, logger) and returns: features