code: def testConfusionMatrixAtThresholds(self):
      computations = (
          confusion_matrix_metrics.ConfusionMatrixAtThresholds(
              thresholds=[0.3, 0.5, 0.8]
          ).computations(example_weighted=True)
      )
      histogram = computations[0]
      matrices = computations[1]
      metrics = computations[2]
  
      example1 = {
          "labels": np.array([0.0]),
          "predictions": np.array([0.0]),
          "example_weights": np.array([1.0]),
      }
      example2 = {
          "labels": np.array([0.0]),
          "predictions": np.array([0.5]),
          "example_weights": np.array([1.0]),
      }
      example3 = {
          "labels": np.array([1.0]),
          "predictions": np.array([0.3]),
          "example_weights": np.array([1.0]),
      }
      example4 = {
          "labels": np.array([1.0]),
          "predictions": np.array([0.9]),
          "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, example4])
              | "Process"
              >> beam.Map(metric_util.to_standard_metric_inputs)
              | "AddSlice" >> beam.Map(lambda x: ((), x))
              | "ComputeHistogram"
              >> beam.CombinePerKey(histogram.combiner)
              | "ComputeMatrices"
              >> beam.Map(lambda x: (x[0], matrices.result(x[1])))
              | "ComputeMetrics"
              >> beam.Map(lambda x: (x[0], metrics.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, ())
                  self.assertLen(got_metrics, 1)
                  key = metric_types.MetricKey(
                      name="confusion_matrix_at_thresholds",
                      example_weighted=True,
                  )
                  self.assertIn(key, got_metrics)
                  got_metric = got_metrics[key]
                  self.assertEqual(
                      binary_confusion_matrices.Matrices(
                          thresholds=[0.3, 0.5, 0.8],
                          tp=[1.0, 1.0, 1.0],
                          tn=[1.0, 2.0, 2.0],
                          fp=[1.0, 0.0, 0.0],
                          fn=[1.0, 1.0, 1.0],
                      ),
                      got_metric,
                  )
  
              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 9 for calling the ConfusionMatrixMetricsTest obj's underlying member method code with example usage: obj.testConfusionMatrixAtThresholds() without return types