code: def setUp(self):
      super(MaskSplitterNetworkTest, self).setUp()
      self._observation_and_mask_spec = {
          "observation": tensor_spec.BoundedTensorSpec(
              (2,), tf.float32, 0, 5
          ),
          "mask": tensor_spec.BoundedTensorSpec((3,), tf.int32, 0, 1),
      }
      self._observation_spec = self._observation_and_mask_spec[
          "observation"
      ]
      self._mask_spec = self._observation_and_mask_spec["mask"]
      self._state_spec = tensor_spec.BoundedTensorSpec(
          (1,), tf.int64, 0, 10
      )
  
      def splitter_fn(observation_and_mask):
          return (
              observation_and_mask["observation"],
              observation_and_mask["mask"],
          )
  
      self._splitter_fn = splitter_fn
      self._observation_and_mask = tensor_spec.sample_spec_nest(
          self._observation_and_mask_spec, outer_dims=(4,)
      )
      self._network_state = tensor_spec.sample_spec_nest(
          self._state_spec, outer_dims=(4,)
      )
  
      self._output_spec = distribution_spec.DistributionSpec(
          tfp.distributions.Categorical,
          self._observation_spec,
          sample_spec=tensor_spec.BoundedTensorSpec(
              (1,), tf.int64, 0, 1
          ),
          **tfp.distributions.Categorical(logits=[0, 5]).parameters,
      )
  
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 1 for calling the MaskSplitterNetworkTest obj's underlying member method code with example usage: obj.setUp() and returns: observation_and_mask,  observation_and_mask