code: def testDtypePreservation(self, dtype):
      dist = cholesky_lkj.CholeskyLKJ(2, dtype([1.0]))
      x = dist.sample(seed=test_util.test_seed())
      lp = dist.log_prob(x)
      self.assertEqual(dtype, x.dtype)
      self.assertEqual(dtype, lp.dtype)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import cholesky_outer_product
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import square
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import lkj
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the CholeskyLKJTest obj's underlying member method code with example usage: obj.testDtypePreservation(dtype) without return types