code: def testInitParameterVariations(self, noise_kwargs, implied_values):
      num_test_points = 3
      num_obs_points = 4
      kernel = exponentiated_quadratic.ExponentiatedQuadratic()
      index_points = np.random.uniform(-1.0, 1.0, (num_test_points, 1))
      observation_index_points = np.random.uniform(
          -1.0, 1.0, (num_obs_points, 1)
      )
      observations = np.random.uniform(-1.0, 1.0, num_obs_points)
      jitter = 1e-6
  
      dist = gprm.GaussianProcessRegressionModel(
          kernel=kernel,
          index_points=index_points,
          observation_index_points=observation_index_points,
          observations=observations,
          jitter=jitter,
          validate_args=True,
          **noise_kwargs,
      )
  
      # 'Property' means what was passed to CTOR. 'Parameter' is the effective
      # value by which the distribution is parameterized.
      implied_onv_param = implied_values[
          "observation_noise_variance_parameter"
      ]
      implied_pnv_param = implied_values[
          "predictive_noise_variance_parameter"
      ]
  
      # k_xx - k_xn @ (k_nn + ONV) @ k_nx + PNV
      k = lambda x, y: _np_kernel_matrix_fn(1.0, 1.0, x, y)
      k_tt_ = k(index_points, index_points)
      k_tx_ = k(index_points, observation_index_points)
      k_xx_plus_noise_ = k(
          observation_index_points, observation_index_points
      ) + (jitter + implied_onv_param) * np.eye(num_obs_points)
  
      expected_predictive_covariance = (
          k_tt_
          - np.dot(k_tx_, np.linalg.solve(k_xx_plus_noise_, k_tx_.T))
          + implied_pnv_param * np.eye(num_test_points)
      )
  
      # Assertion 1: predictive covariance is correct.
      self.assertAllClose(
          self.evaluate(dist.covariance()),
          expected_predictive_covariance,
      )
  
      # Assertion 2: predictive_noise_variance property is correct
      self.assertIsInstance(dist.predictive_noise_variance, tf.Tensor)
      self.assertAllClose(
          self.evaluate(dist.predictive_noise_variance),
          implied_pnv_param,
      )
  
      # Assertion 3: observation_noise_variance property is correct.
      self.assertIsInstance(dist.observation_noise_variance, tf.Tensor)
      self.assertAllClose(
          self.evaluate(dist.observation_noise_variance),
          # Note that this is, somewhat unintuitively, expceted to equal the
          # predictive_noise_variance. This is because of 1) the inheritance
          # structure of GPRM as a subclass of GaussianProcess and 2) the poor
          # choice of name of the GaussianProcess noise parameter. The latter
          # issue is being cleaned up in cl/256413439.
          implied_pnv_param,
      )
  
imports: from unittest import mock
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exp_sin_squared
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 9 for calling the _GaussianProcessRegressionModelTest obj's underlying member method code with example usage: obj.testInitParameterVariations(noise_kwargs, implied_values) without return types