code: def testSquaredPearsonCorrelationWithoutWeights(self):
      computations = (
          squared_pearson_correlation.SquaredPearsonCorrelation().computations()
      )
      metric = computations[0]
  
      example1 = {
          "labels": np.array([2.0]),
          "predictions": np.array([1.0]),
          "example_weights": np.array([1.0]),
      }
      example2 = {
          "labels": np.array([1.0]),
          "predictions": np.array([2.0]),
          "example_weights": np.array([1.0]),
      }
      example3 = {
          "labels": np.array([2.0]),
          "predictions": np.array([3.0]),
          "example_weights": np.array([1.0]),
      }
      example4 = {
          "labels": np.array([3.0]),
          "predictions": np.array([4.0]),
          "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, example4])
              | "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]
                  # 1: prediction = 1, label = 2
                  # 2: prediction = 2, label = 1
                  # 3: prediction = 3, label = 2
                  # 4: prediction = 4, label = 3
                  #
                  # pred_x_labels = 2 + 2 + 6 + 12 = 22
                  # labels = 2 + 1 + 2 + 3 =  8
                  # preds = 1 + 2 + 3 + 4 = 10
                  # sq_labels = 4 + 1 + 4 + 9 = 18
                  # sq_preds = 1 + 4 + 9 + 16 = 30
                  # examples = 4
                  #
                  # r^2 = (22 - 8 * 10 / 4)^2 / (30 - 10^2 / 4) * (18 - 8^2 / 4)
                  # r^2 = 4 / (5 * 2) = 0.4
                  self.assertDictElementsAlmostEqual(
                      got_metrics, {key: 0.4}, places=5
                  )
  
              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 1 for calling the SquaredPearsonCorrelationTest obj's underlying member method code with example usage: obj.testSquaredPearsonCorrelationWithoutWeights() without return types