code: def test_TransformObservations(self) -> None:
      transformed_obsd_mid = self.t._transform_observation_data(
          [deepcopy(self.obsd_mid)]
      )[0]
      # Approximate assertion for robustness.
      mean_results = np.array(list(transformed_obsd_mid.means))
      expected = np.array([0, 1.28155])
      self.assertTrue(
          np.allclose(mean_results, expected),
          msg=f"Mean Results: {mean_results}. Expected {expected}.",
      )
  
      cov_results = np.array(transformed_obsd_mid.covariance)
      expected = np.array([[0.0327499, 0.0], [0.0, 0.3684465]])
      self.assertTrue(
          np.allclose(cov_results, expected),
          msg=f"Covariance Result: {cov_results}. Expected {expected}.",
      )
  
      # Fail with extreme values.
      with self.assertRaises(ValueError):
          self.t._transform_observation_data(
              [deepcopy(self.obsd_extreme)]
          )[0]
  
      # NaN covar values remain as NaNs
      transformed_obsd_nan_covars = self.t._transform_observation_data(
          [deepcopy(self.obsd_nan_covars)]
      )[0]
      cov_results = np.array(transformed_obsd_nan_covars.covariance)
      self.assertTrue(
          np.all(np.isnan(cov_results)),
          msg=f"Unexpected covariance Result: {cov_results}. Expected all nans.",
      )
  
file_path: Ax/ax/modelbridge/transforms/tests/test_inverse_gaussian_cdf_y_transform.py
imports: from copy import deepcopy
  import numpy
  from ax.core.observation import ObservationData
  from ax.modelbridge.transforms.inverse_gaussian_cdf_y import InverseGaussianCdfY
  from ax.utils.common.testutils import TestCase
label_desc: Use image node_id 2 for calling the InverseGaussianCdfYTransformTest obj's underlying member method code with example usage: obj.test_TransformObservations() without return types