code: def _get_partitioned_statistics_summary(
      statistics: Dict[types.FeaturePath, Dict[Text, np.ndarray]]
  ) -> Dict[types.FeaturePath, Dict[Text, float]]:
      """Computes meta-statistics over the custom stats in the input dict."""
  
      summary = collections.defaultdict(collections.defaultdict)
      for feature_path, feature_statistics in statistics.items():
          summary_for_feature = summary[feature_path]
          for stat_name, stat_values in feature_statistics.items():
              summary_for_feature["min_" + stat_name] = np.min(
                  stat_values
              )
              summary_for_feature["max_" + stat_name] = np.max(
                  stat_values
              )
              summary_for_feature["mean_" + stat_name] = np.mean(
                  stat_values
              )
              summary_for_feature["median_" + stat_name] = np.median(
                  stat_values
              )
              summary_for_feature["std_dev_" + stat_name] = np.std(
                  stat_values
              )
              summary_for_feature[
                  "num_partitions_" + stat_name
              ] = stat_values.size
      return summary
  
imports: import collections
  import functools
  from typing import Dict, Iterable, Text, Tuple
  import apache_beam
  import numpy
  import pyarrow
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import stats_generator
  from tensorflow_data_validation.utils import stats_util
  from tfx_bsl.arrow import table_util
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 1 for calling a global function with example usage: _get_partitioned_statistics_summary(statistics) and returns: summary