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
      )
  
      # ==> shape = [6, 25, 2]
      if not self.is_static:
          index_points_1 = tf1.placeholder_with_default(
              index_points_1, shape=None
          )
          index_points_2 = tf1.placeholder_with_default(
              index_points_2, shape=None
          )
  
      mean_fn = lambda x: np.array([0.0], np.float32)
      kernel_1 = psd_kernels.ExponentiatedQuadratic()
      kernel_2 = psd_kernels.ExpSinSquared()
  
      tp1 = student_t_process.StudentTProcess(
          df=3.0,
          kernel=kernel_1,
          index_points=index_points_1,
          mean_fn=mean_fn,
          jitter=1e-5,
          validate_args=True,
      )
      tp2 = tp1.copy(
          df=4.0, index_points=index_points_2, kernel=kernel_2
      )
  
      event_shape_1 = [5]
      event_shape_2 = [10]
  
      self.assertEqual(tp1.mean_fn, tp2.mean_fn)
      self.assertIsInstance(
          tp1.kernel, psd_kernels.ExponentiatedQuadratic
      )
      self.assertIsInstance(tp2.kernel, psd_kernels.ExpSinSquared)
  
      if self.is_static or tf.executing_eagerly():
          self.assertAllEqual(tp1.batch_shape, tp2.batch_shape)
          self.assertAllEqual(tp1.event_shape, event_shape_1)
          self.assertAllEqual(tp2.event_shape, event_shape_2)
          self.assertEqual(self.evaluate(tp1.df), 3.0)
          self.assertEqual(self.evaluate(tp2.df), 4.0)
          self.assertAllEqual(tp2.index_points, index_points_2)
          self.assertAllEqual(tp1.index_points, index_points_1)
          self.assertAllEqual(tp2.index_points, index_points_2)
          self.assertAllEqual(
              tf.get_static_value(tp1.jitter),
              tf.get_static_value(tp2.jitter),
          )
      else:
          self.assertAllEqual(
              self.evaluate(tp1.batch_shape_tensor()),
              self.evaluate(tp2.batch_shape_tensor()),
          )
          self.assertAllEqual(
              self.evaluate(tp1.event_shape_tensor()), event_shape_1
          )
          self.assertAllEqual(
              self.evaluate(tp2.event_shape_tensor()), event_shape_2
          )
          self.assertEqual(
              self.evaluate(tp1.jitter), self.evaluate(tp2.jitter)
          )
          self.assertEqual(self.evaluate(tp1.df), 3.0)
          self.assertEqual(self.evaluate(tp2.df), 4.0)
          self.assertAllEqual(
              self.evaluate(tp1.index_points), index_points_1
          )
          self.assertAllEqual(
              self.evaluate(tp2.index_points), index_points_2
          )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import multivariate_student_t
  from tensorflow_probability.python.distributions import student_t_process
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import psd_kernels
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 4 for calling the _StudentTProcessTest obj's underlying member method code with example usage: obj.testCopy() without return types