code: def testVariableParametersArePreserved(self, process_name, data):
      # Check that the process passes Variables through to the accessor
      # properties (without converting them to Tensor or anything like that).
      process = data.draw(
          stochastic_processes(process_name, enable_vars=True)
      )
      self.evaluate([var.initializer for var in process.variables])
  
      for k, v in six.iteritems(process.parameters):
          if not tensor_util.is_ref(v):
              continue
          self.assertIs(
              getattr(process, k),
              v,
              "Parameter equivalance assertion failed for parameter `{}`".format(
                  k
              ),
          )
  
imports: from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import six
  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.distributions import student_t_process
  from tensorflow_probability.python.distributions import student_t_process_regression_model
  from tensorflow_probability.python.distributions import variational_gaussian_process
  from tensorflow_probability.python.experimental.distributions import marginal_fns
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import hypothesis_testlib
label_desc: Use image node_id 1 for calling the StochasticProcessParamsAreVarsTest obj's underlying member method code with example usage: obj.testVariableParametersArePreserved(process_name, data) without return types