code: def test_Transform(self) -> None:
      obs_feats1 = ObservationFeatures(parameters={"a": 0.0})
      with self.assertRaisesRegex(
          RuntimeError,
          "MergeRepeatedMeasurements requires observations",
      ):
          # test that observations are required
          MergeRepeatedMeasurements()
      # test nan in covariance
      observation = Observation(
          data=ObservationData(
              metric_names=["m1"],
              means=np.array([1.0]),
              covariance=np.array([[float("nan")]]),
          ),
          features=obs_feats1,
      )
      with self.assertRaisesRegex(
          NotImplementedError,
          "All metrics must have noise observations.",
      ):
          MergeRepeatedMeasurements(observations=[observation])
      # test full covariance
      observation = Observation(
          data=ObservationData(
              metric_names=["m1", "m2"],
              means=np.array([1.0, 1.0]),
              covariance=np.ones((2, 2)),
          ),
          features=obs_feats1,
      )
      with self.assertRaisesRegex(
          NotImplementedError,
          "Only independent metrics are currently supported.",
      ):
          MergeRepeatedMeasurements(observations=[observation])
  
      # test noiseless, different means
      zero_covar = np.zeros((1, 1))
      observations = [
          Observation(
              data=ObservationData(
                  metric_names=["m1"],
                  means=np.array([1.0]),
                  covariance=zero_covar,
              ),
              features=obs_feats1,
          ),
          Observation(
              data=ObservationData(
                  metric_names=["m1"],
                  means=np.array([2.0]),
                  covariance=zero_covar,
              ),
              features=obs_feats1,
          ),
      ]
      with self.assertRaisesRegex(
          ValueError,
          "All repeated arms with noiseless measurements "
          "must have the same means.",
      ):
          MergeRepeatedMeasurements(observations=observations)
      # test noiseless, same means
      observations = [
          Observation(
              data=ObservationData(
                  metric_names=["m1"],
                  means=np.array([1.0]),
                  covariance=zero_covar,
              ),
              features=obs_feats1,
          ),
          Observation(
              data=ObservationData(
                  metric_names=["m1"],
                  means=np.array([1.0]),
                  covariance=zero_covar,
              ),
              features=obs_feats1,
          ),
          Observation(
              data=ObservationData(
                  metric_names=["m1"],
                  means=np.array([2.0]),
                  covariance=zero_covar,
              ),
              features=ObservationFeatures(parameters={"a": 2.0}),
          ),
      ]
      t = MergeRepeatedMeasurements(observations=observations)
      expected_obs = observations[-2:]
      transformed_obs = t.transform_observations(observations)
      for i in (0, 1):
          compare_obs(
              test=self,
              obs1=expected_obs[i],
              obs2=transformed_obs[i],
              discrepancy_tol=0.0,
          )
  
      # basic test
      obs_feat1 = ObservationFeatures(parameters={"a": 0.0, "b": 1.0})
      obs1 = Observation(
          data=ObservationData(
              metric_names=["m1", "m2"],
              means=np.array([1.0, 2.0]),
              covariance=np.array(
                  [
                      [1.0, 0.0],
                      [0.0, 2.0],
                  ]
              ),
          ),
          features=obs_feat1,
      )
      obs2 = Observation(
          data=ObservationData(
              metric_names=["m1", "m2"],
              means=np.array([1.0, 1.0]),
              covariance=np.array(
                  [
                      [1.0, 0.0],
                      [0.0, 3.0],
                  ]
              ),
          ),
          features=obs_feat1,
      )
      # different arm
      obs3 = Observation(
          data=ObservationData(
              metric_names=["m1", "m2"],
              means=np.array([3.0, 1.0]),
              covariance=np.array(
                  [
                      [4.0, 0.0],
                      [0.0, 5.0],
                  ]
              ),
          ),
          features=ObservationFeatures(parameters={"a": 1.0, "b": 0.0}),
      )
      expected_obs = Observation(
          data=ObservationData(
              metric_names=["m1", "m2"],
              means=np.array([1.0, 1.6]),
              covariance=np.array([[0.5, 0.0], [0.0, 1.2]]),
          ),
          features=obs_feat1,
      )
      observations = [obs1, obs2, obs3]
      t = MergeRepeatedMeasurements(observations=observations)
      observations2 = t.transform_observations(observations)
      compare_obs(
          test=self,
          obs1=expected_obs,
          obs2=observations2[0],
          discrepancy_tol=1e-8,
      )
      compare_obs(
          test=self,
          obs1=obs3,
          obs2=observations2[1],
          discrepancy_tol=0.0,
      )
  
imports: import numpy
  from ax.core.observation import Observation, ObservationData, ObservationFeatures
  from ax.modelbridge.transforms.merge_repeated_measurements import MergeRepeatedMeasurements
  from ax.utils.common.testutils import TestCase
label_desc: Use image node_id 1 for calling the MergeRepeatedMeasurementsTransformTest obj's underlying member method code with example usage: obj.test_Transform() without return types