code: def testGradientWorksDespiteBijectorCaching(self):
      x = tf.constant(2.0)
      fn_result, grads = util.maybe_call_fn_and_grads(
          lambda x_: lognormal.LogNormal(loc=0.0, scale=1.0).log_prob(
              x_
          ),
          x,
      )
      self.assertAllEqual(False, fn_result is None)
      self.assertAllEqual([False], [g is None for g in grads])
  
imports: import collections
  import warnings
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 2 for calling the GradientTest obj's underlying member method code with example usage: obj.testGradientWorksDespiteBijectorCaching() without return types