code: def testTrainAgentScoreFeedback(
      self,
      policy_type,
      batch_size,
      global_dim,
      item_dim,
      num_items,
      num_slots,
      non_click_score,
      loss,
  ):
      if not tf.executing_eagerly():
          self.skipTest("Only works in eager mode.")
      obs_spec = bandit_spec_utils.create_per_arm_observation_spec(
          global_dim, item_dim, num_items, add_num_actions_feature=True
      )
      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 = tensor_spec.TensorSpec(
          shape=(num_slots,), 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,
      )
      if non_click_score is not None:
          with self.assertRaisesRegex(
              ValueError, "Parameter `non_click_score`"
          ):
              _ = 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.SCORE_VECTOR,
                  non_click_score=non_click_score,
                  optimizer=optimizer,
              )
          non_click_score = None
  
      agent = ranking_agent.RankingAgent(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          scoring_network=scoring_net,
          policy_type=policy_type,
          error_loss_fn=(
              tf.compat.v1.losses.sigmoid_cross_entropy
              if loss == "sigmoid_cross_entropy"
              else tf.compat.v1.losses.mean_squared_error
          ),
          feedback_model=ranking_agent.FeedbackModel.SCORE_VECTOR,
          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],
      )
      num_actions = tf.constant(
          num_slots - 1, shape=[batch_size], dtype=tf.int32
      )
  
      observations = {
          bandit_spec_utils.GLOBAL_FEATURE_KEY: global_obs,
          bandit_spec_utils.PER_ARM_FEATURE_KEY: item_obs,
          bandit_spec_utils.NUM_ACTIONS_FEATURE_KEY: num_actions,
      }
      scores = tf.reshape(
          tf.range(batch_size * num_slots, dtype=tf.float32),
          shape=[batch_size, num_slots],
      )
      if loss == "sigmoid_cross_entropy":
          scores = tf.where(
              tf.greater(scores, tf.reduce_mean(scores)),
              tf.ones_like(scores, dtype=tf.float32),
              tf.zeros_like(scores, 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, num_slots],
              )
          ),
      )
      experience = _get_experience(
          initial_step, action_step, final_step
      )
      for i in range(10):
          self.assertGreaterEqual(
              agent.train(experience).loss, 0, msg=f"Train step {i}"
          )
  
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 2 for calling the RankingAgentTest obj's underlying member method code with example usage: obj.testTrainAgentScoreFeedback(policy_type, batch_size, global_dim, item_dim, num_items, num_slots, non_click_score, loss) without return types