code: def example_to_feature(example_row):
      """
      :param example_row: tuple (example, label_map, tokenizer, max_seq_length)
      :return: InputFeatures
      """
  
      (example, label_map, tokenizer, max_seq_length) = example_row
  
      try:
          tokens_a = tokenizer.tokenize(example.text_a)
      except Exception as e:
          tokens_a = []
  
      # Account for [CLS] and [SEP] with "- 2"
      if len(tokens_a) > max_seq_length - 2:
          tokens_a = tokens_a[: (max_seq_length - 2)]
  
      tokens = tokens_a + [tokenizer.sep_token]
      segment_ids = [0] * len(tokens)
  
      tokens = [tokenizer.cls_token] + tokens
      segment_ids = [0] + 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] * len(input_ids)
  
      # Zero-pad up to the sequence length.
      padding_length = max_seq_length - len(input_ids)
  
      input_ids = input_ids + ([0] * padding_length)
      input_mask = input_mask + ([0] * padding_length)
      segment_ids = segment_ids + ([0] * padding_length)
  
      assert len(input_ids) == max_seq_length
      assert len(input_mask) == max_seq_length
      assert len(segment_ids) == max_seq_length
  
      label_id = label_map[example.label]
  
      return InputFeatures(
          input_ids=input_ids,
          input_mask=input_mask,
          segment_ids=segment_ids,
          label_ids=label_id,
      )
  
imports: from __future__ import print_function, unicode_literals
  import random
  from contextlib import closing
  from multiprocessing import Pool
  import numpy
  import pandas
  import torch
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, random_split
  from torchtext.legacy import data
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: example_to_feature(example_row) and returns: InputFeatures