code: def test_cascading_to_scorevector(self):
      batch_size = 5
      global_dim = 12
      item_dim = 4
      num_items = 23
      num_slots = 9
  
      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=ranking_environment.FeedbackModel.SCORE_VECTOR,
          click_model=ranking_environment.ClickModel.DISTANCE_BASED,
          distance_threshold=10.0,
          batch_size=batch_size,
      )
  
      chosen_items = np.array([0, 2, 9, 1, 2])
      chosen_values = np.array([6, 8, 4, 2, 3])
      score_vector = env._cascading_to_scorevector(
          chosen_items, chosen_values
      )
      self.assertAllEqual(score_vector.shape, [batch_size, num_slots])
  
      # The third row is all zeros because `chosen_item == 9` means no click.
      self.assertAllEqual(
          score_vector,
          [
              [6, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 8, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 2, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 3, 0, 0, 0, 0, 0, 0],
          ],
      )
  
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 2 for calling the RankingPyEnvironmentTest obj's underlying member method code with example usage: obj.test_cascading_to_scorevector() and returns: np, np