code: def testWrongOutputLayerRaiseError(self):
      action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 10, 20)
      policy = greedy_multi_objective_policy.GreedyMultiObjectiveNeuralPolicy(
          self._time_step_spec,
          action_spec,
          self._scalarizer,
          self._create_objective_networks(),
      )
      observations = tf.constant([[1, 2], [2, 1]], dtype=tf.float32)
      time_step = ts.restart(observations, batch_size=2)
      with self.assertRaisesRegexp(
          ValueError,
          r"The number of actions \(11\) does not match objective network 0"
          r" output size \(3\)\.",
      ):
          policy.action(time_step)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Dict, List, Text, Tuple
  import numpy
  import tensorflow
  from tf_agents.bandits.multi_objective import multi_objective_scalarizer
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import greedy_multi_objective_neural_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import test_utils
label_desc: Use image node_id 9 for calling the GreedyRewardPredictionPolicyTest obj's underlying member method code with example usage: obj.testWrongOutputLayerRaiseError() without return types