code: def ProtoFromTfRecordFiles(
      files,
      max_entries=10000,
      features=None,
      is_sequence=False,
      iterator_options=None,
  ):
      """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.
      """
      warnings.warn(
          "Code in this directory is deprecated. Use the facets-overview pip package instead.",
          DeprecationWarning,
      )
      return FeatureStatisticsGenerator().ProtoFromTfRecordFiles(
          files, max_entries, features, is_sequence, iterator_options
      )
  
imports: import warnings
  from base_feature_statistics_generator import BaseFeatureStatisticsGenerator
  import feature_statistics_pb2
label_desc: Use image node_id 1 for calling a global function with example usage: ProtoFromTfRecordFiles(files, max_entries, features, is_sequence, iterator_options) and returns: FeatureStatisticsGenerator