code: def testGradientThroughRate(self):
      rate = tf.Variable(3.0)
      d = gamma_lib.Gamma(
          concentration=1.0, rate=rate, validate_args=True
      )
      with tf.GradientTape() as tape:
          loss = -d.log_prob([1.0, 2.0, 4.0])
      grad = tape.gradient(loss, d.trainable_variables)
      self.assertLen(grad, 1)
      self.assertAllNotNone(grad)
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  from scipy import misc
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import implementation_selection
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 32 for calling the GammaTest obj's underlying member method code with example usage: obj.testGradientThroughRate() without return types