code: def test_obtain_skew_sample(self):
      baseline_examples, test_examples, skew_pairs = get_test_input(
          include_skewed_features=True, include_close_floats=False
      )
  
      sample_size = 1
      potential_samples = skew_pairs
      with beam.Pipeline(
          runner=beam_runner_util.get_test_runner()
      ) as p:
          baseline_examples = p | "Create Base" >> beam.Create(
              baseline_examples
          )
          test_examples = p | "Create Test" >> beam.Create(
              test_examples
          )
          _, skew_sample = _unpack_results(
              (baseline_examples, test_examples)
              | feature_skew_detector.DetectFeatureSkewImpl(
                  [_IDENTIFIER1, _IDENTIFIER2],
                  [_IGNORE_FEATURE],
                  sample_size,
              )
          )
          # Because serialization of tf.Examples is not deterministic, we remove the
          # base/test fields of the skew pair before comparing them to the expected
          # samples.
          skew_sample |= "RemoveSelectedFields" >> beam.Map(
              _remove_fields_from_skew_pair
          )
          util.assert_that(
              skew_sample,
              make_sample_equal_fn(
                  self, sample_size, potential_samples
              ),
          )
  
imports: import traceback
  from absl.testing import absltest
  from absl.testing import parameterized
  import apache_beam
  from apache_beam.testing import util
  import tensorflow
  from tensorflow_data_validation.utils import beam_runner_util
  from tensorflow_data_validation.skew import feature_skew_detector
  from tensorflow_data_validation.skew.protos import feature_skew_results_pb2
  from tensorflow_data_validation.utils import test_util
  from google.protobuf import text_format
label_desc: Use image node_id 3 for calling the FeatureSkewDetectorTest obj's underlying member method code with example usage: obj.test_obtain_skew_sample() without return types