code: def testDimensionGuard(self, dtype):
      testee_lkj = cholesky_lkj.CholeskyLKJ(
          dimension=3, concentration=dtype([1.0, 4.0])
      )
      with self.assertRaisesRegexp(ValueError, "dimension mismatch"):
          testee_lkj.log_prob(tf.eye(4))
  
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 3 for calling the CholeskyLKJTest obj's underlying member method code with example usage: obj.testDimensionGuard(dtype) without return types