code: def testObservationShapeMismatch(
      self, batch_size, exploration_strategy, alpha
  ):
      policy = linear_policy.LinearBanditPolicy(
          self._action_spec,
          self._a,
          self._b,
          self._num_samples_per_arm,
          self._time_step_spec,
          exploration_strategy,
          alpha=alpha,
      )
  
      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"Global observation shape is expected to be \[None, 2\]."
          r" Got \[%d, 3\]." % batch_size,
      ):
          policy.action(current_time_step)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.policies import linear_bandit_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 12 for calling the LinearBanditPolicyTest obj's underlying member method code with example usage: obj.testObservationShapeMismatch(batch_size, exploration_strategy, alpha) without return types