code: def testObservationShapeMismatch(
      self, batch_size, actions_from_reward_layer
  ):
      policy = neural_linucb_policy.NeuralLinUCBPolicy(
          DummyNet(self._obs_spec),
          self._encoding_dim,
          get_reward_layer(),
          actions_from_reward_layer=actions_from_reward_layer,
          cov_matrix=self._a,
          data_vector=self._b,
          num_samples=self._num_samples_per_arm,
          epsilon_greedy=0.0,
          time_step_spec=self._time_step_spec,
      )
  
      current_time_step = ts.TimeStep(
          tf.constant(
              ts.StepType.FIRST,
              dtype=tf.int32,
              shape=[batch_size],
              name="step_type",
          ),
          tf.constant(
              0.0, dtype=tf.float32, shape=[batch_size], name="reward"
          ),
          tf.constant(
              1.0, dtype=tf.float32, shape=[batch_size], name="discount"
          ),
          tf.constant(
              np.array(range(batch_size * (self._obs_dim + 1))),
              dtype=tf.float32,
              shape=[batch_size, self._obs_dim + 1],
              name="observation",
          ),
      )
      with self.assertRaisesRegex(
          ValueError, r"\(%d, 3\)" % batch_size
      ):
          policy.action(current_time_step)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.policies import neural_linucb_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 13 for calling the NeuralLinUCBPolicyTest obj's underlying member method code with example usage: obj.testObservationShapeMismatch(batch_size, actions_from_reward_layer) without return types