code: def testGradientsThroughParams(self):
      logits = tf.Variable(
          np.zeros((3, 5, 2)),
          dtype=tf.float32,
          shape=tf.TensorShape([None, None, 2]),
      )
      concentration = tf.Variable(
          np.ones((3, 5, 4)),
          dtype=tf.float32,
          shape=tf.TensorShape(None),
      )
      loc = tf.Variable(
          np.zeros((3, 5, 4)),
          dtype=tf.float32,
          shape=tf.TensorShape(None),
      )
      scale_diag = tf.Variable(
          np.ones((3, 5, 4)),
          dtype=tf.float32,
          shape=tf.TensorShape(None),
      )
  
      dist = mixture.Mixture(
          categorical.Categorical(logits=logits),
          components=[
              dirichlet.Dirichlet(concentration),
              mvn_diag.MultivariateNormalDiag(
                  loc=loc, scale_diag=scale_diag
              ),
          ],
          validate_args=True,
      )
  
      with tf.GradientTape() as tape:
          loss = tf.reduce_sum(dist.log_prob(tf.ones((3, 5, 4)) / 4.0))
      grad = tape.gradient(loss, dist.trainable_variables)
      self.assertLen(grad, 4)
      self.assertAllNotNone(grad)
  
imports: import contextlib
  import warnings
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import mixture
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 26 for calling the MixtureTest obj's underlying member method code with example usage: obj.testGradientsThroughParams() without return types