code: def testWrongOutputLayerRaiseError(self, policy_class):
      tf.compat.v1.set_random_seed(1)
      action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 10, 20)
      policy = policy_class(
          self._time_step_spec,
          action_spec,
          reward_network=DummyNet(self._obs_spec),
      )
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_step = ts.restart(observations, batch_size=2)
      with self.assertRaisesRegex(
          ValueError,
          r"The number of actions \(11\) does not match the reward_network output"
          r" size \(3\)\.",
      ):
          policy.action(time_step, seed=1)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow
  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 boltzmann_reward_prediction_policy
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import falcon_reward_prediction_policy
  from tf_agents.bandits.policies import greedy_reward_prediction_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 5 for calling the RewardPredictionPoliciesTest obj's underlying member method code with example usage: obj.testWrongOutputLayerRaiseError(policy_class) without return types