code: def ProtoFromTfRecordFiles(files,
                             max_entries=10000,
                             features=None,
                             is_sequence=False,
                             iterator_options=None):
    
    warnings.warn(
        'Use GenericFeatureStatisticsGenerator class method instead.',
        DeprecationWarning)
    return FeatureStatisticsGenerator().ProtoFromTfRecordFiles(
        files, max_entries, features, is_sequence, iterator_options)
docs: Creates a feature statistics proto from a set of TFRecord files.
  
    Args:
      files: A list of dicts describing files for each dataset for the proto.
        Each
          entry contains a 'path' field with the path to the TFRecord file on
            disk
          and a 'name' field to identify the dataset in the proto.
      max_entries: The maximum number of examples to load from each dataset
          in order to create the proto. Defaults to 10000.
      features: A list of strings that is a whitelist of feature names to create
          feature statistics for. If set to None then all features in the
            dataset
          are analyzed. Defaults to None.
      is_sequence: True if the input data from 'tables' are tf.SequenceExamples,
          False if tf.Examples. Defaults to false.
      iterator_options: Options to pass to the iterator that reads the examples.
          Defaults to None.
  
    Returns:
      The feature statistics proto for the provided files.