code: def get_test_input(include_skewed_features, include_close_floats):
      baseline_examples = list()
      test_examples = list()
      skew_pairs = list()
      for i in range(_IDENTIFIER_RANGE):
          for j in range(_IDENTIFIER_RANGE):
              shared_example = tf.train.Example()
              shared_example.features.feature[
                  _IDENTIFIER1
              ].int64_list.value.append(i)
              shared_example.features.feature[
                  _IDENTIFIER2
              ].int64_list.value.append(j)
              shared_example.features.feature[
                  _NO_SKEW_FEATURE
              ].int64_list.value.append(1)
  
              base_example = tf.train.Example()
              base_example.CopyFrom(shared_example)
              test_example = tf.train.Example()
              test_example.CopyFrom(shared_example)
  
              base_example.features.feature[
                  _IGNORE_FEATURE
              ].int64_list.value.append(0)
              test_example.features.feature[
                  _IGNORE_FEATURE
              ].int64_list.value.append(1)
  
          if include_close_floats:
              base_example.features.feature[
                  _CLOSE_FLOAT_FEATURE
              ].float_list.value.append(1.12345)
              test_example.features.feature[
                  _CLOSE_FLOAT_FEATURE
              ].float_list.value.append(1.12456)
  
          if include_skewed_features:
              # Add three different kinds of skew: value mismatch, appears only in
              # base, and appears only in test.
              base_example.features.feature[
                  _SKEW_FEATURE
              ].int64_list.value.append(0)
              test_example.features.feature[
                  _SKEW_FEATURE
              ].int64_list.value.append(1)
              base_example.features.feature[
                  _BASE_ONLY_FEATURE
              ].int64_list.value.append(0)
              test_example.features.feature[
                  _TEST_ONLY_FEATURE
              ].int64_list.value.append(1)
  
              skew_pair = feature_skew_results_pb2.SkewPair()
              # Because serialization of tf.Examples is not deterministic, we do not add
              # or compare the base/test fields of the skew pair in this test.
              skew_pair.matched_features.append(_NO_SKEW_FEATURE)
              skew_pair.mismatched_features.append(_SKEW_FEATURE)
              skew_pair.base_only_features.append(_BASE_ONLY_FEATURE)
              skew_pair.test_only_features.append(_TEST_ONLY_FEATURE)
              skew_pairs.append(skew_pair)
  
          baseline_examples.append(base_example)
          test_examples.append(test_example)
      return (baseline_examples, test_examples, skew_pairs)
  
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 4 for calling a global function with example usage: get_test_input(include_skewed_features, include_close_floats) and returns: baseline_examples,  test_examples,  skew_pairs