code: def test_action(self, multithreading, num_policies):
      policy = self._make_batched_py_policy(
          multithreading, num_policies=num_policies
      )
      time_steps = np.array(
          [
              ts.restart(observation=np.array([1]))
              for _ in range(num_policies)
          ],
          dtype=object,
      )
  
      # Call policy.action() and assert PolicySteps are batched correctly.
      policy_step = policy.action(time_steps)
      self.assertEqual(num_policies, policy_step.action.shape[0])
  
      # Take another step and assert that actions have the same shape.
      policy_step2 = policy.action(time_steps)
      self.assertAllEqual(
          policy_step.action.shape[0], policy_step2.action.shape[0]
      )
  
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.policies import batched_py_policy
  from tf_agents.policies import py_policy
  from tf_agents.specs import array_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
label_desc: Use image node_id 8 for calling the BatchedPyPolicyTest obj's underlying member method code with example usage: obj.test_action(multithreading, num_policies) without return types