code: def testNumActionsPolicy(self, batch_size, num_items, num_slots):
      obs_spec = bandit_spec_utils.create_per_arm_observation_spec(
          7, 5, num_items, add_num_actions_feature=True
      )
      time_step_spec = ts.time_step_spec(obs_spec)
      network = arm_net.create_feed_forward_common_tower_network(
          obs_spec, [3], [4], [5]
      )
  
      policy = ranking_policy.PenalizeCosineDistanceRankingPolicy(
          num_items=num_items,
          num_slots=num_slots,
          time_step_spec=time_step_spec,
          network=network,
          penalty_mixture_coefficient=0.3,
      )
      observation = tensor_spec.sample_spec_nest(
          obs_spec, outer_dims=[batch_size]
      )
      time_spec = ts.restart(observation, batch_size=batch_size)
      action_step = policy.action(time_spec)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.assertAllEqual(
          action_step.action.shape, [batch_size, num_slots]
      )
  
file_path: agents/tf_agents/bandits/policies/ranking_policy_test.py
imports: from absl.testing import parameterized
  import tensorflow
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.policies import ranking_policy
  from tf_agents.specs import bandit_spec_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 3 for calling the RankingPolicyTest obj's underlying member method code with example usage: obj.testNumActionsPolicy(batch_size, num_items, num_slots) without return types