code: def testBinaryConfusionMatrices(self, kwargs, expected_matrices):
      computations = (
          binary_confusion_matrices.binary_confusion_matrices(**kwargs)
      )
      histogram = computations[0]
      matrices = computations[1]
  
      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])))
          )  # pyformat: disable
  
          # 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)
                  name_args = {
                      "example_id_key": None,
                      "example_ids_count": None,
                  }
                  if "num_thresholds" in kwargs:
                      thresholds = binary_confusion_matrices._interpolated_thresholds(
                          kwargs["num_thresholds"]
                      )
                      name_args["num_thresholds"] = kwargs[
                          "num_thresholds"
                      ]
                  else:
                      thresholds = kwargs["thresholds"]
                      name_args["thresholds"] = thresholds
  
                  name = metric_util.generate_private_name_from_arguments(
                      binary_confusion_matrices.BINARY_CONFUSION_MATRICES_NAME,
                      **name_args,
                  )
  
                  matrices_key = metric_types.MetricKey(name=name)
                  self.assertIn(matrices_key, got_metrics)
                  got_matrices = got_metrics[matrices_key]
                  self.assertEqual(got_matrices, expected_matrices)
  
              except AssertionError as err:
                  raise util.BeamAssertException(err)
  
          util.assert_that(result, check_result, label="result")
  
file_path: model-analysis/tensorflow_model_analysis/metrics/binary_confusion_matrices_test.py
imports: 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 metric_types
  from tensorflow_model_analysis.metrics import metric_util
label_desc: Use image node_id 2 for calling the BinaryConfusionMatricesTest obj's underlying member method code with example usage: obj.testBinaryConfusionMatrices(kwargs, expected_matrices) without return types