code: def _get_action_step(
      action, num_agents, num_actions, emit_policy_info
  ):
      batch_size = tf.shape(action)[0]
      choices = tf.constant(
          num_agents - 1, shape=action.shape, dtype=tf.int32
      )
      predicted_rewards_mean = (
          tf.zeros([batch_size, num_actions])
          if emit_policy_info
          else ()
      )
      return policy_step.PolicyStep(
          action=tf.convert_to_tensor(action),
          info={
              mixture_policy.MIXTURE_AGENT_ID: choices,
              mixture_policy.SUBPOLICY_INFO: policy_utilities.PolicyInfo(
                  predicted_rewards_mean=predicted_rewards_mean
              ),
          },
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.agents import exp3_mixture_agent
  from tf_agents.bandits.agents import lin_ucb_agent
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.policies import mixture_policy
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling a global function with example usage: _get_action_step(action, num_agents, num_actions, emit_policy_info) and returns: policy_step