code: def testGetMissingSlices(self):
      slicing_specs = [
          config_pb2.SlicingSpec(),
          config_pb2.SlicingSpec(feature_values={"feature1": "value1"}),
          config_pb2.SlicingSpec(feature_values={"feature2": "value2"}),
      ]
      threshold = config_pb2.MetricThreshold(
          value_threshold=config_pb2.GenericValueThreshold(
              upper_bound={"value": 1}
          )
      )
      eval_config = config_pb2.EvalConfig(
          model_specs=[
              config_pb2.ModelSpec(),
          ],
          slicing_specs=slicing_specs,
          metrics_specs=[
              config_pb2.MetricsSpec(
                  metrics=[
                      config_pb2.MetricConfig(
                          class_name="WeightedExampleCount",
                          # 1.5 < 1, NOT OK.
                          per_slice_thresholds=[
                              config_pb2.PerSliceMetricThreshold(
                                  slicing_specs=slicing_specs,
                                  threshold=threshold,
                              )
                          ],
                      ),
                  ],
                  model_names=[""],
                  example_weights=config_pb2.ExampleWeightOptions(
                      weighted=True
                  ),
              ),
          ],
      )
      sliced_metrics = (
          (("feature1", "value1"),),
          {
              metric_types.MetricKey(
                  name="weighted_example_count", example_weighted=True
              ): 0,
          },
      )
      result = metrics_validator.validate_metrics(
          sliced_metrics, eval_config
      )
  
      expected_checks = text_format.Parse(
          """
          validation_ok: true
          validation_details {
            slicing_details {
              slicing_spec {
                feature_values {
                  key: "feature1"
                  value: "value1"
                }
              }
              num_matching_slices: 1
            }
          }""",
          validation_result_pb2.ValidationResult(),
      )
  
      self.assertProtoEquals(expected_checks, result)
  
      missing = metrics_validator.get_missing_slices(
          result.validation_details.slicing_details, eval_config
      )
      self.assertLen(missing, 2)
      self.assertProtoEquals(missing[0], slicing_specs[0])
      self.assertProtoEquals(missing[1], slicing_specs[2])
  
imports: from absl.testing import parameterized
  import tensorflow
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.evaluators import metrics_validator
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import validation_result_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from google.protobuf import text_format
label_desc: Use image node_id 18 for calling the MetricsValidatorTest obj's underlying member method code with example usage: obj.testGetMissingSlices() without return types