code: def test_function_with_variable(self):
      loc = tf.Variable(3.0)
      dist = AutoIndependent(
          AutoNormal(loc, scale=tf.ones([3])),
          reinterpreted_batch_ndims=1,
      )
  
      new_loc = 32.0
  
      @tf.function
      def f(d):
          d.distribution.loc.assign(new_loc)
          self.assertLen(d.trainable_variables, 1)
          return d
  
      dist_ = f(dist)
      self.evaluate(loc.initializer)
      self.assertEqual(self.evaluate(dist_.distribution.loc), new_loc)
      self.assertEqual(self.evaluate(dist.distribution.loc), new_loc)
      self.assertLen(dist.trainable_variables, 1)
  
imports: import functools
  import os
  from absl import flags
  from absl.testing import parameterized
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import transform_diagonal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.experimental.distributions import mvn_precision_factor_linop
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import test_util
  from absl.testing import absltest
label_desc: Use image node_id 12 for calling the AutoCompositeTensorTest obj's underlying member method code with example usage: obj.test_function_with_variable() and returns: d