code: def test_multiply_can_be_trained(self):
      # TODO(thomaswc): Restore this test when bayeux is available.
      seed = jax.random.PRNGKey(20231018)
      x_train, y_train = util.load_fake_dataset()
  
      leaf1 = kernels.PeriodicBNN(
          width=5, period=0.1, going_to_be_multiplied=True
      )
      leaf2 = kernels.LinearBNN(width=5, going_to_be_multiplied=True)
      bnn = operators.Multiply(bnns=[leaf1, leaf2])
  
      init_seed, seed = jax.random.split(seed)
      init_params = bnn.init(init_seed, x_train)
  
      def train_density(params):
          return bnn.log_prob(params, x_train, y_train)
  
      transform, inverse_transform, _ = util.make_transforms(bnn)
      del transform
      del inverse_transform
      del init_params
      del train_density
  
file_path: probability/tensorflow_probability/python/experimental/autobnn/operators_test.py
imports: from absl.testing import parameterized
  import jax
  import jax.numpy
  import numpy
  from tensorflow_probability.python.experimental.autobnn import kernels
  from tensorflow_probability.python.experimental.autobnn import operators
  from tensorflow_probability.python.experimental.autobnn import util
  from tensorflow_probability.substrates.jax.distributions import distribution
  from absl.testing import absltest
label_desc: Use image node_id 7 for calling the OperatorsTest obj's underlying member method code with example usage: obj.test_multiply_can_be_trained() and returns: bnn