code: def testValidateConcentrationAfterMutation(self, dtype):
      dimension = 3
      concentration = tf.Variable(1.5, dtype=dtype)
      d = cholesky_lkj.CholeskyLKJ(
          dimension, concentration, validate_args=True
      )
      self.evaluate([v.initializer for v in d.variables])
      with self.assertRaisesOpError(
          "Argument `concentration` must be >= 1."
      ):
          with tf.control_dependencies([concentration.assign(0.5)]):
              self.evaluate(d.sample(seed=test_util.test_seed()))
  
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 8 for calling the CholeskyLKJTest obj's underlying member method code with example usage: obj.testValidateConcentrationAfterMutation(dtype) without return types