code: def testGradients(self):
      x = tf.Variable([1.0, 1.1, 1.2])
      d = dirichlet.Dirichlet(concentration=x, validate_args=True)
      with tf.GradientTape() as tape:
          loss = -d.log_prob([0.1, 0.2, 0.7])
      g = tape.gradient(loss, d.trainable_variables)
      self.assertLen(g, 1)
      self.assertAllNotNone(g)
  
imports: import numpy
  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 dirichlet
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 1 for calling the DirichletFromVariableTest obj's underlying member method code with example usage: obj.testGradients() without return types