class_docstr: Tests the correctness of the CorrelationCholesky bijector.
class_docstr_tok: ["Tests","the","correctness","of","the","CorrelationCholesky","bijector","."]
code: def testBijectiveWithLKJSamples(self, dimension, concentration):
      bijector = correlation_cholesky.CorrelationCholesky()
      lkj_dist = lkj.LKJ(
          dimension=dimension,
          concentration=np.float64(concentration),
          input_output_cholesky=True,
      )
      batch_size = 10
      y = self.evaluate(
          lkj_dist.sample([batch_size], seed=test_util.test_seed())
      )
      x = self.evaluate(bijector.inverse(y))
  
      bijector_test_util.assert_bijective_and_finite(
          bijector,
          x,
          y,
          eval_func=self.evaluate,
          event_ndims=1,
          inverse_event_ndims=2,
          rtol=1e-5,
      )
  
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import correlation_cholesky
  from tensorflow_probability.python.bijectors import fill_triangular
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import lkj
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import transformed_kernel
label_desc: Use image node_id 8 for calling the CorrelationCholeskyBijectorTest obj's underlying member method code with example usage: obj.testBijectiveWithLKJSamples(dimension, concentration) without return types