code: def _compute_skew_for_examples(
      base_example: tf.train.Example,
      test_example: tf.train.Example,
      features_to_ignore: List[tf.train.Feature],
      float_round_ndigits: Optional[int],
  ) -> Tuple[_PerFeatureSkew, bool]:
      """Computes feature skew for a pair of baseline and test examples.
  
      Args:
        base_example: The baseline example to compare.
        test_example: The test example to compare.
        features_to_ignore: The features not to compare.
        float_round_ndigits: Number of digits precision after the decimal point to
          which to round float values before comparison.
  
      Returns:
        A tuple containing a list of the skew information for each feature
        and a boolean indicating whether skew was found in any feature, in which
        case the examples are considered skewed.
      """
      all_feature_names = set()
      all_feature_names.update(base_example.features.feature.keys())
      all_feature_names.update(test_example.features.feature.keys())
      feature_names = all_feature_names.difference(
          set(features_to_ignore)
      )
  
      result = list()
      is_skewed = False
      for name in feature_names:
          base_feature = base_example.features.feature.get(name)
          test_feature = test_example.features.feature.get(name)
          skew = _compute_skew_for_features(
              base_feature, test_feature, float_round_ndigits, name
          )
          if skew.match_count == 0:
              # If any features have a mismatch or are found only in the baseline or
              # test example, the examples are considered skewed.
              is_skewed = True
          result.append((name, skew))
      return result, is_skewed
  
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 3 for calling a global function with example usage: _compute_skew_for_examples(base_example, test_example, features_to_ignore, float_round_ndigits) and returns: result,  is_skewed