code: def _get_action_step(action, num_agents, num_actions):
      batch_size = tf.shape(action)[0]
      choices = tf.random.uniform(
          shape=tf.shape(action),
          minval=0,
          maxval=num_agents - 1,
          dtype=tf.int32,
      )
      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=tf.zeros(
                      [batch_size, num_actions]
                  )
              ),
          },
      )
  
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 lin_ucb_agent
  from tf_agents.bandits.agents import mixture_agent
  from tf_agents.bandits.agents import neural_epsilon_greedy_agent
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.policies import mixture_policy
  from tf_agents.networks import q_network
  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 3 for calling a global function with example usage: _get_action_step(action, num_agents, num_actions) and returns: policy_step