code: def _compute_skew_for_features(
      base_feature: tf.train.Feature,
      test_feature: tf.train.Feature,
      float_round_ndigits: Optional[int],
      feature_name: str,
  ) -> feature_skew_results_pb2.FeatureSkew:
      """Computes feature skew for a pair of baseline and test features.
  
      Args:
        base_feature: The feature to compare from the baseline example.
        test_feature: The feature to compare from the test example.
        float_round_ndigits: Number of digits precision after the decimal point to
          which to round float values before comparison.
        feature_name: The name of the feature for which to compute skew between the
          examples.
  
      Returns:
        A FeatureSkew proto containing information about skew for the specified
          feature.
      """
      skew_results = feature_skew_results_pb2.FeatureSkew()
      skew_results.feature_name = feature_name
      if not _empty_or_null(base_feature) and not _empty_or_null(
          test_feature
      ):
          skew_results.base_count = 1
          skew_results.test_count = 1
          if farmhash.fingerprint64(
              _get_serialized_feature(base_feature, float_round_ndigits)
          ) == farmhash.fingerprint64(
              _get_serialized_feature(test_feature, float_round_ndigits)
          ):
              skew_results.match_count = 1
          else:
              skew_results.mismatch_count = 1
      elif not _empty_or_null(base_feature):
          skew_results.base_count = 1
          skew_results.base_only = 1
      elif not _empty_or_null(test_feature):
          skew_results.test_count = 1
          skew_results.test_only = 1
      elif (test_feature is None) == (base_feature is None):
          # Both features are None, or present with zero values.
          skew_results.match_count = 1
      return skew_results
  
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 2 for calling a global function with example usage: _compute_skew_for_features(base_feature, test_feature, float_round_ndigits, feature_name) and returns: skew_results