code: def _construct_skew_pair(
      per_feature_skew: List[
          Tuple[str, feature_skew_results_pb2.FeatureSkew]
      ],
      base_example: tf.train.Example,
      test_example: tf.train.Example,
  ) -> feature_skew_results_pb2.SkewPair:
      """Constructs a SkewPair from baseline and test examples.
  
      Args:
        per_feature_skew: Skew results for each feature in the input examples.
        base_example: The baseline example to include.
        test_example: The test example to include.
  
      Returns:
        A SkewPair containing examples that exhibit some skew.
      """
      skew_pair = feature_skew_results_pb2.SkewPair()
      skew_pair.base = base_example.SerializeToString()
      skew_pair.test = test_example.SerializeToString()
  
      for feature_name, skew_result in per_feature_skew:
          if skew_result.match_count == 1:
              skew_pair.matched_features.append(feature_name)
          elif skew_result.base_only == 1:
              skew_pair.base_only_features.append(feature_name)
          elif skew_result.test_only == 1:
              skew_pair.test_only_features.append(feature_name)
          elif skew_result.mismatch_count == 1:
              skew_pair.mismatched_features.append(feature_name)
  
      return skew_pair
  
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 5 for calling a global function with example usage: _construct_skew_pair(per_feature_skew, base_example, test_example) and returns: skew_pair