code: def convert_single_example(
      ex_index, example, label_list, max_seq_length, tokenizer
  ):
      """Converts a single `InputExample` into a single `InputFeatures`."""
      label_map = {}
      for i, label in enumerate(label_list):
          label_map[label] = i
  
      input_ids, segment_ids, input_mask = tokenizer.encode_text(
          text_a=example.text_a,
          text_b=example.text_b,
          max_seq_length=max_seq_length,
      )
  
      label_id = label_map[example.label]
  
      # here we disable the verbose printing of the data
      if ex_index < 0:
          tf.logging.info("*** Example ***")
          tf.logging.info("guid: %s" % example.guid)
          tf.logging.info(
              "input_ids: %s" % " ".join([str(x) for x in input_ids])
          )
          tf.logging.info("input_ids length: %d" % len(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,
      )
      return feature
  
imports: import os
  import csv
  import collections
  import tensorflow
  import texar.tf
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