code: def testConfusionMatrixMetricsWithFractionalLabels(
      self, metric, expected_value
  ):
      computations = metric.computations(example_weighted=True)
      histogram = computations[0]
      matrix = computations[1]
      derived_metric = computations[2]
  
      # The following examples will be expanded to:
      #
      # prediction | label | weight
      #     0.0    |   -   |  1.0
      #     0.7    |   -   |  0.4
      #     0.7    |   +   |  0.6
      #     1.0    |   -   |  0.2
      #     1.0    |   +   |  0.8
      example1 = {
          "labels": np.array([0.0]),
          "predictions": np.array([0.0]),
          "example_weights": np.array([1.0]),
      }
      example2 = {
          "labels": np.array([0.6]),
          "predictions": np.array([0.7]),
          "example_weights": np.array([1.0]),
      }
      example3 = {
          "labels": np.array([0.8]),
          "predictions": np.array([1.0]),
          "example_weights": np.array([1.0]),
      }
  
      with beam.Pipeline() as pipeline:
          # pylint: disable=no-value-for-parameter
          result = (
              pipeline
              | "Create" >> beam.Create([example1, example2, example3])
              | "Process"
              >> beam.Map(metric_util.to_standard_metric_inputs)
              | "AddSlice" >> beam.Map(lambda x: ((), x))
              | "ComputeHistogram"
              >> beam.CombinePerKey(histogram.combiner)
              | "ComputeConfusionMatrix"
              >> beam.Map(lambda x: (x[0], matrix.result(x[1])))
              | "ComputeMetric"
              >> beam.Map(lambda x: (x[0], derived_metric.result(x[1])))
          )
  
          # pylint: enable=no-value-for-parameter
  
          def check_result(got):
              try:
                  self.assertLen(got, 1)
                  got_slice_key, got_metrics = got[0]
                  self.assertEqual(got_slice_key, ())
                  key = metric_types.MetricKey(
                      name=metric.name, example_weighted=True
                  )
                  self.assertDictElementsAlmostEqual(
                      got_metrics, {key: expected_value}, places=5
                  )
  
              except AssertionError as err:
                  raise util.BeamAssertException(err)
  
          util.assert_that(result, check_result, label="result")
  
imports: import math
  from absl.testing import parameterized
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.metrics import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import confusion_matrix_metrics
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
label_desc: Use image node_id 4 for calling the ConfusionMatrixMetricsTest obj's underlying member method code with example usage: obj.testConfusionMatrixMetricsWithFractionalLabels(metric, expected_value) without return types