code: def testCopy(self):
      # 5 random index points in R^2
      index_points_1 = np.random.uniform(-4.0, 4.0, (5, 2)).astype(
          np.float32
      )
      # 10 random index points in R^2
      index_points_2 = np.random.uniform(-4.0, 4.0, (10, 2)).astype(
          np.float32
      )
  
      observation_index_points_1 = np.random.uniform(
          -4.0, 4.0, (7, 2)
      ).astype(np.float32)
      observation_index_points_2 = np.random.uniform(
          -4.0, 4.0, (9, 2)
      ).astype(np.float32)
  
      observations_1 = np.random.uniform(-1.0, 1.0, 7).astype(
          np.float32
      )
      observations_2 = np.random.uniform(-1.0, 1.0, 9).astype(
          np.float32
      )
  
      # ==> shape = [6, 25, 2]
      mean_fn = lambda x: np.array([0.0], np.float32)
      kernel_1 = exponentiated_quadratic.ExponentiatedQuadratic()
      kernel_2 = exp_sin_squared.ExpSinSquared()
  
      dist1 = stprm.StudentTProcessRegressionModel(
          df=5.0,
          kernel=kernel_1,
          index_points=index_points_1,
          observation_index_points=observation_index_points_1,
          observations=observations_1,
          mean_fn=mean_fn,
          validate_args=True,
      )
      dist2 = dist1.copy(
          kernel=kernel_2,
          index_points=index_points_2,
          observation_index_points=observation_index_points_2,
          observations=observations_2,
      )
  
      precomputed_dist1 = stprm.StudentTProcessRegressionModel.precompute_regression_model(
          df=5.0,
          kernel=kernel_1,
          index_points=index_points_1,
          observation_index_points=observation_index_points_1,
          observations=observations_1,
          mean_fn=mean_fn,
          validate_args=True,
      )
      precomputed_dist2 = precomputed_dist1.copy(
          index_points=index_points_2
      )
      self.assertIs(
          precomputed_dist1.mean_fn, precomputed_dist2.mean_fn
      )
      self.assertIs(precomputed_dist1.kernel, precomputed_dist2.kernel)
  
      event_shape_1 = [5]
      event_shape_2 = [10]
  
      self.assertIsInstance(
          dist1.kernel.schur_complement.base_kernel,
          exponentiated_quadratic.ExponentiatedQuadratic,
      )
      self.assertIsInstance(
          dist2.kernel.schur_complement.base_kernel,
          exp_sin_squared.ExpSinSquared,
      )
      self.assertAllEqual(
          self.evaluate(dist1.batch_shape_tensor()),
          self.evaluate(dist2.batch_shape_tensor()),
      )
      self.assertAllEqual(
          self.evaluate(dist1.event_shape_tensor()), event_shape_1
      )
      self.assertAllEqual(
          self.evaluate(dist2.event_shape_tensor()), event_shape_2
      )
      self.assertAllEqual(
          self.evaluate(dist1.index_points), index_points_1
      )
      self.assertAllEqual(
          self.evaluate(dist2.index_points), index_points_2
      )
  
imports: from unittest import mock
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import student_t_process
  from tensorflow_probability.python.distributions import student_t_process_regression_model
  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 7 for calling the StudentTProcessRegressionModelTest obj's underlying member method code with example usage: obj.testCopy() without return types