class_docstr: LightGCN model
  
  :Citation:
  
      He, Xiangnan, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang.
      "LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation." arXiv
      preprint arXiv:2002.02126, 2020.
docstr: Score all items for test users.
  
  Args:
      user_ids (np.array): Users to test.
      remove_seen (bool): Flag to remove items seen in training from recommendation.
  
  Returns:
      numpy.ndarray: Value of interest of all items for the users.
docstr_tok: ["Score","all","items","for","test","users",".","Args",":","user_ids","(","np.array",")",":","Users","to","test",".","remove_seen","(","bool",")",":","Flag","to","remove","items","seen","in","training","from","recommendation",".","Returns",":","numpy.ndarray",":","Value","of","interest","of","all","items","for","the","users","."]
code: def score(self, user_ids, remove_seen=True):
      """Score all items for test users.
  
      Args:
          user_ids (np.array): Users to test.
          remove_seen (bool): Flag to remove items seen in training from recommendation.
  
      Returns:
          numpy.ndarray: Value of interest of all items for the users.
  
      """
      if any(np.isnan(user_ids)):
          raise ValueError(
              "LightGCN cannot score users that are not in the training set"
          )
      u_batch_size = self.batch_size
      n_user_batchs = len(user_ids) // u_batch_size + 1
      test_scores = []
      for u_batch_id in range(n_user_batchs):
          start = u_batch_id * u_batch_size
          end = (u_batch_id + 1) * u_batch_size
          user_batch = user_ids[start:end]
          item_batch = range(self.data.n_items)
          rate_batch = self.sess.run(
              self.batch_ratings,
              {self.users: user_batch, self.pos_items: item_batch},
          )
          test_scores.append(np.array(rate_batch))
      test_scores = np.concatenate(test_scores, axis=0)
      if remove_seen:
          test_scores += self.data.R.tocsr()[user_ids, :] * -np.inf
      return test_scores
  
imports: import tensorflow
  import time
  import os
  import sys
  import numpy
  import pandas
  from recommenders.evaluation.python_evaluation import map_at_k, ndcg_at_k, precision_at_k, recall_at_k
  from recommenders.utils.python_utils import get_top_k_scored_items
label_desc: Use image node_id 9 for calling the LightGCN obj's underlying member method code with example usage: obj.score(user_ids, remove_seen) and returns: test_scores