code: def testTrainAgentCascadingFeedback(
      self,
      policy_type,
      batch_size,
      global_dim,
      item_dim,
      num_items,
      num_slots,
      non_click_score,
      loss,
  ):
      obs_spec = bandit_spec_utils.create_per_arm_observation_spec(
          global_dim, item_dim, num_items
      )
      scoring_net = global_and_arm_feature_network.create_feed_forward_common_tower_network(
          obs_spec, (4, 3), (3, 4), (4, 2)
      )
      optimizer = tf.compat.v1.train.GradientDescentOptimizer(
          learning_rate=0.1
      )
      reward_spec = {
          ranking_agent.CHOSEN_INDEX: tensor_spec.BoundedTensorSpec(
              shape=[], dtype=tf.int32, minimum=0, maximum=num_slots
          ),
          ranking_agent.CHOSEN_VALUE: tensor_spec.TensorSpec(
              shape=(), dtype=tf.float32
          ),
      }
      time_step_spec = ts.time_step_spec(
          obs_spec, reward_spec=reward_spec
      )
      action_spec = tensor_spec.BoundedTensorSpec(
          shape=(num_slots,),
          minimum=0,
          maximum=num_items - 1,
          dtype=tf.int32,
      )
      agent = ranking_agent.RankingAgent(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          scoring_network=scoring_net,
          policy_type=policy_type,
          feedback_model=ranking_agent.FeedbackModel.CASCADING,
          non_click_score=non_click_score,
          optimizer=optimizer,
      )
      global_obs = tf.reshape(
          tf.range(batch_size * global_dim, dtype=tf.float32),
          [batch_size, global_dim],
      )
      item_obs = tf.reshape(
          tf.range(batch_size * num_slots * item_dim, dtype=tf.float32),
          [batch_size, num_slots, item_dim],
      )
      observations = {
          bandit_spec_utils.GLOBAL_FEATURE_KEY: global_obs,
          bandit_spec_utils.PER_ARM_FEATURE_KEY: item_obs,
      }
      scores = {
          ranking_agent.CHOSEN_INDEX: tf.range(batch_size),
          ranking_agent.CHOSEN_VALUE: tf.range(
              batch_size, dtype=tf.float32
          ),
      }
      initial_step, final_step = _get_initial_and_final_steps(
          observations, scores
      )
      action_step = policy_step.PolicyStep(
          action=(),
          info=policy_utilities.PolicyInfo(
              predicted_rewards_mean=tf.reshape(
                  tf.range(batch_size * num_slots, dtype=tf.float32),
                  shape=[batch_size, 1, num_slots],
              )
          ),
      )
      experience = _get_experience(
          initial_step, action_step, final_step
      )
      loss_info = agent.train(experience, None)
      self.assertAllEqual(loss_info.loss.shape, ())
  
file_path: agents/tf_agents/bandits/agents/ranking_agent_test.py
imports: from absl.testing import parameterized
  import tensorflow
  from tf_agents.bandits.agents import ranking_agent
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.policies import utils
  from tf_agents.specs import bandit_spec_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 1 for calling the RankingAgentTest obj's underlying member method code with example usage: obj.testTrainAgentCascadingFeedback(policy_type, batch_size, global_dim, item_dim, num_items, num_slots, non_click_score, loss) without return types