code: def test_ranking_environment(
      self,
      batch_size,
      global_dim,
      item_dim,
      num_items,
      num_slots,
      feedback_model,
      click_model,
      real_cascade,
  ):
      def _global_sampling_fn():
          return np.random.randint(-10, 10, [global_dim])
  
      def _item_sampling_fn():
          return np.random.randint(-2, 3, [item_dim])
  
      scores_weight_matrix = (
          np.reshape(
              np.arange(global_dim * item_dim, dtype=float),
              newshape=[item_dim, global_dim],
          )
          - 10
      ) / 5
  
      env = ranking_environment.RankingPyEnvironment(
          _global_sampling_fn,
          _item_sampling_fn,
          num_items=num_items,
          num_slots=num_slots,
          scores_weight_matrix=scores_weight_matrix,
          feedback_model=feedback_model,
          click_model=click_model,
          real_cascade=real_cascade,
          distance_threshold=10.0,
          batch_size=batch_size,
      )
      time_step_spec = env.time_step_spec()
      action_spec = env.action_spec()
  
      random_policy = random_py_policy.RandomPyPolicy(
          time_step_spec=time_step_spec, action_spec=action_spec
      )
  
      for _ in range(5):
          time_step = env.reset()
          self.assertTrue(
              check_unbatched_time_step_spec(
                  time_step=time_step,
                  time_step_spec=time_step_spec,
                  batch_size=env.batch_size,
              )
          )
  
          action = random_policy.action(time_step).action
          self.assertAllEqual(action.shape, [batch_size, num_slots])
          self.assertAllGreaterEqual(action, 0)
          time_step = env.step(action)
          reward = time_step.reward
          if (
              feedback_model
              == ranking_environment.FeedbackModel.CASCADING
          ):
              self.assertAllEqual(
                  reward["chosen_index"].shape, [batch_size]
              )
              self.assertAllGreaterEqual(reward["chosen_index"], 0)
              self.assertAllEqual(
                  reward["chosen_value"].shape, [batch_size]
              )
          else:
              self.assertAllEqual(reward.shape, [batch_size, num_slots])
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.environments import ranking_environment
  from tf_agents.policies import random_py_policy
  from tf_agents.specs import array_spec
label_desc: Use image node_id 1 for calling the RankingPyEnvironmentTest obj's underlying member method code with example usage: obj.test_ranking_environment(batch_size, global_dim, item_dim, num_items, num_slots, feedback_model, click_model, real_cascade) and returns: np, np