code: def testSquaredPearsonCorrelationMetricsWithNan(self):
      computations = (
          squared_pearson_correlation.SquaredPearsonCorrelation().computations()
      )
      metric = computations[0]
  
      example = {
          "labels": np.array([0.0]),
          "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([example])
              | "Process"
              >> beam.Map(metric_util.to_standard_metric_inputs)
              | "AddSlice" >> beam.Map(lambda x: ((), x))
              | "ComputeMetric" >> beam.CombinePerKey(metric.combiner)
          )
  
          # 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.keys[0]
                  self.assertIn(key, got_metrics)
                  self.assertTrue(math.isnan(got_metrics[key]))
  
              except AssertionError as err:
                  raise util.BeamAssertException(err)
  
          util.assert_that(result, check_result, label="result")
  
file_path: model-analysis/tensorflow_model_analysis/metrics/squared_pearson_correlation_test.py
imports: import math
  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_util
  from tensorflow_model_analysis.metrics import squared_pearson_correlation
label_desc: Use image node_id 3 for calling the SquaredPearsonCorrelationTest obj's underlying member method code with example usage: obj.testSquaredPearsonCorrelationMetricsWithNan() without return types