code: def testCalculateConfidenceInterval(self):
      sampling_data_list = [
          np.array(
              [
                  [0, 0, 2, 7, 0.77777779, 1],
                  [1, 0, 2, 6, 0.75, 0.85714287],
                  [4, 0, 2, 3, 0.60000002, 0.42857143],
                  [4, 2, 0, 3, 1, 0.42857143],
                  [7, 2, 0, 0, float("nan"), 0],
              ]
          ),
          np.array(
              [
                  [7, 2, 0, 0, float("nan"), 0],
                  [0, 0, 2, 7, 0.77777779, 1],
                  [1, 0, 2, 6, 0.75, 0.85714287],
                  [4, 0, 2, 3, 0.60000002, 0.42857143],
                  [4, 2, 0, 3, 1, 0.42857143],
              ]
          ),
      ]
      unsampled_data = np.array(
          [
              [4, 2, 0, 3, 1, 0.42857143],
              [7, 2, 0, 0, float("nan"), 0],
              [0, 0, 2, 7, 0.77777779, 1],
              [1, 0, 2, 6, 0.75, 0.85714287],
              [4, 0, 2, 3, 0.60000002, 0.42857143],
          ]
      )
      result = poisson_bootstrap._calculate_t_distribution(
          sampling_data_list, unsampled_data
      )
      self.assertIsInstance(result, np.ndarray)
      self.assertEqual(result.shape, (5, 6))
      self.assertAlmostEqual(result[0][0].sample_mean, 3.5, delta=0.1)
      self.assertAlmostEqual(
          result[0][0].sample_standard_deviation, 4.94, delta=0.1
      )
      self.assertEqual(result[0][0].sample_degrees_of_freedom, 1)
      self.assertEqual(result[0][0].unsampled_value, 4.0)
      self.assertAlmostEqual(result[0][4].sample_mean, 0.77, delta=0.1)
      self.assertTrue(np.isnan(result[0][4].sample_standard_deviation))
      self.assertEqual(result[0][4].sample_degrees_of_freedom, 0)
      self.assertEqual(result[0][4].unsampled_value, 1.0)
  
      sampling_data_list = [
          np.array([1, 2]),
          np.array([1, 2]),
          np.array([1, float("nan")]),
      ]
      unsampled_data = np.array([1, 2])
      result = poisson_bootstrap._calculate_t_distribution(
          sampling_data_list, unsampled_data
      )
      self.assertIsInstance(result, np.ndarray)
      self.assertEqual(
          result.tolist(),
          [
              types.ValueWithTDistribution(
                  sample_mean=1.0,
                  sample_standard_deviation=0.0,
                  sample_degrees_of_freedom=2,
                  unsampled_value=1,
              ),
              types.ValueWithTDistribution(
                  sample_mean=2.0,
                  sample_standard_deviation=0.0,
                  sample_degrees_of_freedom=1,
                  unsampled_value=2,
              ),
          ],
      )
  
imports: import numpy
  import tensorflow
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.evaluators import legacy_poisson_bootstrap
label_desc: Use image node_id 1 for calling the PoissonBootstrapTest obj's underlying member method code with example usage: obj.testCalculateConfidenceInterval() without return types