code: def testDistributionNetworkWithPassthroughMask(self):
      # Create a wrapped network.
      wrapped_network = WrappedDistributionNetwork(
          input_tensor_spec=self._observation_spec,
          state_spec=(),
          output_spec=self._output_spec,
          name="WrappedDistributionNetwork",
      )
  
      # Create a splitter network optionally passing the `input_tensor_spec` which
      # always applies the mask (`passthrough_mask=True`).
      splitter_network = mask_splitter_network.MaskSplitterNetwork(
          splitter_fn=self._splitter_fn,
          wrapped_network=wrapped_network,
          passthrough_mask=True,
      )  # The mask is passed through.
  
      # Apply splitter network which returns a distribution based on directly the
      # input `observation`.
      distribution, _ = splitter_network(self._observation_and_mask)
  
      # Check if distribution was properly created based on the input observation.
      logits = self._observation_and_mask["observation"]
      self.assertAllClose(
          logits,
          distribution.parameters["logits"],
      )
      # Check if the wrapped network received the right mask .
      self.assertAllEqual(
          wrapped_network.mask, self._observation_and_mask["mask"]
      )
      # Check if the `output_spec` of the `splitter_network` is equal to the
      # `output_spec` of the `wrapped_network`.
      self.assertEqual(
          splitter_network.output_spec, wrapped_network.output_spec
      )
  
imports: import tensorflow.compat.v2
  import tensorflow_probability
  from tf_agents.networks import mask_splitter_network
  from tf_agents.networks import network
  from tf_agents.networks import value_network
  from tf_agents.specs import distribution_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.utils import test_utils
label_desc: Use image node_id 6 for calling the MaskSplitterNetworkTest obj's underlying member method code with example usage: obj.testDistributionNetworkWithPassthroughMask() without return types