code: def _convert_comments_data_tfrecord(
      input_filename, output_filename=None
  ):
      """Convert the public civil comments data, for tfrecord data."""
      with tf.io.TFRecordWriter(output_filename) as writer:
          for serialized in tf.data.TFRecordDataset(
              filenames=[input_filename]
          ):
              example = tf.train.Example()
              example.ParseFromString(serialized.numpy())
              if not example.features.feature[
                  TEXT_FEATURE
              ].bytes_list.value:
                  continue
  
              new_example = tf.train.Example()
              new_example.features.feature[
                  TEXT_FEATURE
              ].bytes_list.value.extend(
                  example.features.feature[
                      TEXT_FEATURE
                  ].bytes_list.value
              )
              new_example.features.feature[
                  LABEL
              ].float_list.value.append(
                  1
                  if example.features.feature[LABEL].float_list.value[0]
                  >= _THRESHOLD
                  else 0
              )
  
              for (
                  identity_category,
                  identity_list,
              ) in IDENTITY_COLUMNS.items():
                  grouped_identity = []
                  for identity in identity_list:
                      if (
                          example.features.feature[
                              identity
                          ].float_list.value
                          and example.features.feature[
                              identity
                          ].float_list.value[0]
                          >= _THRESHOLD
                      ):
                          grouped_identity.append(identity.encode())
                  new_example.features.feature[
                      identity_category
                  ].bytes_list.value.extend(grouped_identity)
              writer.write(new_example.SerializeToString())
  
      return output_filename
  
file_path: fairness-indicators/fairness_indicators/tutorial_utils/util.py
imports: import os
  import os.path
  import tempfile
  import pandas
  import tensorflow
  import tensorflow_model_analysis
  from google.protobuf import text_format
label_desc: Use image node_id 2 for calling a global function with example usage: _convert_comments_data_tfrecord(input_filename, output_filename) and returns: output_filename