code: def convert_examples_to_features_and_output_to_files(
      examples, label_list, max_seq_length, tokenizer, output_file
  ):
      """Convert a set of `InputExample`s to a TFRecord file."""
  
      writer = tf.python_io.TFRecordWriter(output_file)
  
      for ex_index, example in enumerate(examples):
          feature = convert_single_example(
              ex_index, example, label_list, max_seq_length, tokenizer
          )
  
          def create_int_feature(values):
              return tf.train.Feature(
                  int64_list=tf.train.Int64List(value=list(values))
              )
  
          features = collections.OrderedDict()
          features["input_ids"] = create_int_feature(feature.input_ids)
          features["input_mask"] = create_int_feature(
              feature.input_mask
          )
          features["segment_ids"] = create_int_feature(
              feature.segment_ids
          )
          features["label_ids"] = create_int_feature([feature.label_id])
  
          tf_example = tf.train.Example(
              features=tf.train.Features(feature=features)
          )
          writer.write(tf_example.SerializeToString())
  
imports: import os
  import csv
  import collections
  import tensorflow
  import texar.tf
label_desc: Use image node_id 2 for calling a global function with example usage: convert_examples_to_features_and_output_to_files(examples, label_list, max_seq_length, tokenizer, output_file) and returns: tf