code: def convert_single_example(
      ex_index, example, label_list, max_seq_length, tokenizer
  ):
      """Converts a single `InputExample` into a single `InputFeatures`."""
  
      if isinstance(example, PaddingInputExample):
          return InputFeatures(
              input_ids=[0] * max_seq_length,
              input_mask=[0] * max_seq_length,
              segment_ids=[0] * max_seq_length,
              label_id=0,
              is_real_example=False,
          )
  
      label_map = {}
      for i, label in enumerate(label_list):
          label_map[label] = i
  
      tokens_a = tokenizer.tokenize(example.text_a)
      tokens_b = None
      if example.text_b:
          tokens_b = tokenizer.tokenize(example.text_b)
  
      if tokens_b:
          # Modifies `tokens_a` and `tokens_b` in place so that the total
          # length is less than the specified length.
          # Account for [CLS], [SEP], [SEP] with "- 3"
          _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
      else:
          # Account for [CLS] and [SEP] with "- 2"
          if len(tokens_a) > max_seq_length - 2:
              tokens_a = tokens_a[0 : (max_seq_length - 2)]
  
      # 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 the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens = []
      segment_ids = []
      tokens.append("[CLS]")
      segment_ids.append(0)
      for token in tokens_a:
          tokens.append(token)
          segment_ids.append(0)
      tokens.append("[SEP]")
      segment_ids.append(0)
  
      if tokens_b:
          for token in tokens_b:
              tokens.append(token)
              segment_ids.append(1)
          tokens.append("[SEP]")
          segment_ids.append(1)
  
      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.
      while len(input_ids) < max_seq_length:
          input_ids.append(0)
          input_mask.append(0)
          segment_ids.append(0)
  
      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]
      if ex_index < 5:
          tf.logging.info("*** Example ***")
          tf.logging.info("guid: %s" % (example.guid))
          tf.logging.info(
              "tokens: %s"
              % " ".join(
                  [tokenization.printable_text(x) for x in tokens]
              )
          )
          tf.logging.info(
              "input_ids: %s" % " ".join([str(x) for x in input_ids])
          )
          tf.logging.info(
              "input_mask: %s" % " ".join([str(x) for x in input_mask])
          )
          tf.logging.info(
              "segment_ids: %s"
              % " ".join([str(x) for x in segment_ids])
          )
          tf.logging.info(
              "label: %s (id = %d)" % (example.label, label_id)
          )
  
      feature = InputFeatures(
          input_ids=input_ids,
          input_mask=input_mask,
          segment_ids=segment_ids,
          label_id=label_id,
          is_real_example=True,
      )
      return feature
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import csv
  import os
  import mesh_tensorflow
  import mesh_tensorflow.bert.bert
  import mesh_tensorflow.bert.optimization
  import mesh_tensorflow.bert.tokenization
  from six.moves import range
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling a global function with example usage: convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) and returns: feature, InputFeatures