code: def testMultiClassConfusionMatrixAtThresholds(self, kwargs):
      computations = multi_class_confusion_matrix_metrics.MultiClassConfusionMatrixAtThresholds(
          **kwargs
      ).computations(
          example_weighted=True
      )
      matrices = computations[0]
      metrics = computations[1]
  
      example1 = {
          "labels": np.array([2.0]),
          "predictions": np.array([0.2, 0.3, 0.5]),
          "example_weights": np.array([0.5]),
      }
      example2 = {
          "labels": np.array([0.0]),
          "predictions": np.array([0.1, 0.3, 0.6]),
          "example_weights": np.array([1.0]),
      }
      example3 = {
          "labels": np.array([1.0]),
          "predictions": np.array([0.3, 0.1, 0.6]),
          "example_weights": np.array([0.25]),
      }
      example4 = {
          "labels": np.array([1.0]),
          "predictions": np.array([0.1, 0.9, 0.0]),
          "example_weights": np.array([1.0]),
      }
      example5 = {
          "labels": np.array([1.0]),
          "predictions": np.array([0.1, 0.8, 0.1]),
          "example_weights": np.array([1.0]),
      }
      example6 = {
          "labels": np.array([2.0]),
          "predictions": np.array([0.3, 0.1, 0.6]),
          "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,
                      example5,
                      example6,
                  ]
              )
              | "Process"
              >> beam.Map(metric_util.to_standard_metric_inputs)
              | "AddSlice" >> beam.Map(lambda x: ((), x))
              | "ComputeMatrices"
              >> beam.CombinePerKey(matrices.combiner)
              | "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="multi_class_confusion_matrix_at_thresholds",
                      example_weighted=True,
                  )
                  got_matrix = got_metrics[key]
                  self.assertEqual(
                      multi_class_confusion_matrix_metrics.Matrices(
                          {
                              0.5: {
                                  multi_class_confusion_matrix_metrics.MatrixEntryKey(
                                      actual_class_id=0,
                                      predicted_class_id=2,
                                  ): 1.0,
                                  multi_class_confusion_matrix_metrics.MatrixEntryKey(
                                      actual_class_id=1,
                                      predicted_class_id=1,
                                  ): 2.0,
                                  multi_class_confusion_matrix_metrics.MatrixEntryKey(
                                      actual_class_id=1,
                                      predicted_class_id=2,
                                  ): 0.25,
                                  multi_class_confusion_matrix_metrics.MatrixEntryKey(
                                      actual_class_id=2,
                                      predicted_class_id=-1,
                                  ): 0.5,
                                  multi_class_confusion_matrix_metrics.MatrixEntryKey(
                                      actual_class_id=2,
                                      predicted_class_id=2,
                                  ): 1.0,
                              }
                          }
                      ),
                      got_matrix,
                  )
              except AssertionError as err:
                  raise util.BeamAssertException(err)
  
          util.assert_that(result, check_result, label="result")
  
file_path: model-analysis/tensorflow_model_analysis/metrics/multi_class_confusion_matrix_metrics_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 metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import multi_class_confusion_matrix_metrics
label_desc: Use image node_id 2 for calling the MultiClassConfusionMatrixMetricsTest obj's underlying member method code with example usage: obj.testMultiClassConfusionMatrixAtThresholds(kwargs) without return types