code: def testGetSlicesForFeaturesDictUnivalent(self):
      test_cases = [
          ("Overall", [], [], [()]),
          ("Feature does not match", [], [("age", 99)], []),
          ("No such column", ["no_such_column"], [], []),
          ("Single column", ["age"], [], [(("age", 5),)]),
          ("Single feature", [], [("age", 5)], [(("age", 5),)]),
          (
              "Single feature type mismatch",
              [],
              [("age", "5")],
              [(("age", 5),)],
          ),
          (
              "One column, one feature",
              ["gender"],
              [("age", 5)],
              [(("age", 5), ("gender", "f"))],
          ),
          (
              "Two features",
              ["interest", "gender"],
              [("age", 5)],
              [(("age", 5), ("gender", "f"), ("interest", "cars"))],
          ),
      ]  # pyformat: disable
      features_dict = self._makeFeaturesDict(
          {"gender": ["f"], "age": [5], "interest": ["cars"]}
      )
      for name, columns, features, expected in test_cases:
          self.assertSliceResult(
              name, features_dict, columns, features, expected
          )
  
imports: from absl.testing import parameterized
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import six
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.extractors import slice_key_extractor
  from tensorflow_model_analysis.post_export_metrics import metric_keys
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from google.protobuf import text_format
label_desc: Use image node_id 9 for calling the SlicerTest obj's underlying member method code with example usage: obj.testGetSlicesForFeaturesDictUnivalent() without return types