code: def test_convert_variables_to_tensors(self):
      v = tf.Variable(0.0)
      u = tf.Variable(1.0)
      var_td = transformed_distribution.TransformedDistribution(
          normal.Normal(v, 1.0), bijector=shift.Shift(u)
      )
      tensor_td = var_td._convert_variables_to_tensors()
  
      self.evaluate([x.initializer for x in var_td.trainable_variables])
      self.assertIsInstance(var_td, tf.__internal__.CompositeTensor)
      self.assertLen(var_td.trainable_variables, 2)
      self.assertEmpty(tensor_td.trainable_variables)
      self.assertEqual(
          self.evaluate(var_td.distribution.loc),
          self.evaluate(tensor_td.distribution.loc),
      )
      self.assertEqual(
          self.evaluate(var_td.bijector.shift),
          self.evaluate(tensor_td.bijector.shift),
      )
  
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 23 for calling the AutoCompositeTensorTest obj's underlying member method code with example usage: obj.test_convert_variables_to_tensors() without return types