code: def testFullyReparameterized(self):
      lam = tf.constant([0.1, 1.0])
      _, grad_lam = gradient.value_and_gradient(
          lambda l: exponential.Exponential(
              rate=lam, validate_args=True
          ).sample(  # pylint: disable=g-long-lambda
              100, seed=test_util.test_seed()
          ),
          lam,
      )
      self.assertIsNotNone(grad_lam)
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 10 for calling the ExponentialTest obj's underlying member method code with example usage: obj.testFullyReparameterized() without return types