code: def _merge_feature_skew_results(
      skew_results: Iterable[feature_skew_results_pb2.FeatureSkew],
  ) -> feature_skew_results_pb2.FeatureSkew:
      """Merges multiple FeatureSkew protos into a single FeatureSkew proto.
  
      Args:
        skew_results: An iterable of FeatureSkew protos.
  
      Returns:
        A FeatureSkew proto containing the result of merging the inputs.
      """
      result = feature_skew_results_pb2.FeatureSkew()
      for skew_result in skew_results:
          if not result.feature_name:
              result.feature_name = skew_result.feature_name
          elif result.feature_name != skew_result.feature_name:
              raise ValueError(
                  "Attempting to merge skew results with different names."
              )
          result.base_count += skew_result.base_count
          result.test_count += skew_result.test_count
          result.match_count += skew_result.match_count
          result.base_only += skew_result.base_only
          result.test_only += skew_result.test_only
          result.mismatch_count += skew_result.mismatch_count
      result.diff_count = (
          result.base_only + result.test_only + result.mismatch_count
      )
      return result
  
imports: from typing import Any, Dict, Iterable, Iterator, List, Optional, Tuple, Union
  import apache_beam
  import farmhash
  import numpy
  import tensorflow
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.utils import artifacts_io_impl
  from tensorflow_data_validation.skew.protos import feature_skew_results_pb2
label_desc: Use image node_id 4 for calling a global function with example usage: _merge_feature_skew_results(skew_results) and returns: result