code: def file_based_convert_examples_to_features(
      examples,
      max_seq_length,
      encoder,
      output_file,
      BOS_token="<|endoftext|>",
      EOS_token="<|endoftext|>",
      PAD_token="<|endoftext|>",
  ):
      """Converts a set of examples to a TFRecord file."""
  
      writer = tf.python_io.TFRecordWriter(output_file)
  
      for _, example in enumerate(examples):
          text_ids, length = process_single_text(
              example,
              max_seq_length,
              encoder,
              BOS_token,
              EOS_token,
              PAD_token,
          )
  
          def _create_int_feature(values):
              return tf.train.Feature(
                  int64_list=tf.train.Int64List(value=list(values))
              )
  
          features = collections.OrderedDict()
          features["text_ids"] = _create_int_feature(text_ids)
          features["length"] = _create_int_feature([length])
  
          tf_example = tf.train.Example(
              features=tf.train.Features(feature=features)
          )
          writer.write(tf_example.SerializeToString())
  
imports: import os
  import collections
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: file_based_convert_examples_to_features(examples, max_seq_length, encoder, output_file, BOS_token, EOS_token, PAD_token) and returns: tf