code: def testActionBatchWithBias(
      self, batch_size, exploration_strategy, alpha
  ):
      a = [
          tf.constant(
              [[4, 1, 2], [1, 5, 3], [2, 3, 6]], dtype=tf.float32
          )
      ] * self._num_actions
      b = [
          tf.constant([r, r, r], dtype=tf.float32)
          for r in range(self._num_actions)
      ]
      policy = linear_policy.LinearBanditPolicy(
          self._action_spec,
          a,
          b,
          self._num_samples_per_arm,
          self._time_step_spec,
          exploration_strategy,
          alpha=alpha,
          add_bias=True,
      )
  
      action_step = policy.action(
          self._time_step_batch(batch_size=batch_size)
      )
      self.assertEqual(action_step.action.shape.as_list(), [batch_size])
      self.assertEqual(action_step.action.dtype, tf.int32)
      actions_ = self.evaluate(action_step.action)
      self.assertAllGreaterEqual(actions_, self._action_spec.minimum)
      self.assertAllLessEqual(actions_, self._action_spec.maximum)
  
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 14 for calling the LinearBanditPolicyTest obj's underlying member method code with example usage: obj.testActionBatchWithBias(batch_size, exploration_strategy, alpha) without return types