code: def make_dataset_feature_stats_proto_topk_single(
      feature_path_tuple: types.FeaturePathTuple,
      value_count_list: List[FeatureValueCount],
      is_weighted_stats: bool,
      num_top_values: int,
      frequency_threshold: Union[int, float],
      num_rank_histogram_buckets: int,
  ) -> statistics_pb2.DatasetFeatureStatistics:
      """Makes a DatasetFeatureStatistics proto with top-k stats for a feature."""
      feature_path = types.FeaturePath(feature_path_tuple)
      result = statistics_pb2.DatasetFeatureStatistics()
      result.features.add().CopyFrom(
          _make_feature_stats_proto_topk(
              feature_path,
              value_count_list,
              is_weighted_stats,
              num_top_values,
              frequency_threshold,
              num_rank_histogram_buckets,
          )
      )
      return result
  
file_path: data-validation/tensorflow_data_validation/utils/top_k_uniques_stats_util.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import List, Mapping, Optional, Union
  import apache_beam
  import six
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.utils import stats_util
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 4 for calling a global function with example usage: make_dataset_feature_stats_proto_topk_single(feature_path_tuple, value_count_list, is_weighted_stats, num_top_values, frequency_threshold, num_rank_histogram_buckets) and returns: result