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: Recommend top K items for all users in the test set.
  
  Args:
      test (pandas.DataFrame): Test data.
      top_k (int): Number of top items to recommend.
      sort_top_k (bool): Flag to sort top k results.
      remove_seen (bool): Flag to remove items seen in training from recommendation.
  
  Returns:
      pandas.DataFrame: Top k recommendation items for each user.
docstr_tok: ["Recommend","top","K","items","for","all","users","in","the","test","set",".","Args",":","test","(","pandas.DataFrame",")",":","Test","data",".","top_k","(","int",")",":","Number","of","top","items","to","recommend",".","sort_top_k","(","bool",")",":","Flag","to","sort","top","k","results",".","remove_seen","(","bool",")",":","Flag","to","remove","items","seen","in","training","from","recommendation",".","Returns",":","pandas.DataFrame",":","Top","k","recommendation","items","for","each","user","."]
code: def recommend_k_items(
      self,
      test,
      top_k=10,
      sort_top_k=True,
      remove_seen=True,
      use_id=False,
  ):
      """Recommend top K items for all users in the test set.
  
      Args:
          test (pandas.DataFrame): Test data.
          top_k (int): Number of top items to recommend.
          sort_top_k (bool): Flag to sort top k results.
          remove_seen (bool): Flag to remove items seen in training from recommendation.
  
      Returns:
          pandas.DataFrame: Top k recommendation items for each user.
  
      """
      data = self.data
      if not use_id:
          user_ids = np.array(
              [data.user2id[x] for x in test[data.col_user].unique()]
          )
      else:
          user_ids = np.array(test[data.col_user].unique())
  
      test_scores = self.score(user_ids, remove_seen=remove_seen)
  
      top_items, top_scores = get_top_k_scored_items(
          scores=test_scores, top_k=top_k, sort_top_k=sort_top_k
      )
  
      df = pd.DataFrame(
          {
              data.col_user: np.repeat(
                  test[data.col_user].drop_duplicates().values,
                  top_items.shape[1],
              ),
              data.col_item: top_items.flatten()
              if use_id
              else [data.id2item[item] for item in top_items.flatten()],
              data.col_prediction: top_scores.flatten(),
          }
      )
  
      return df.replace(-np.inf, np.nan).dropna()
  
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 10 for calling the LightGCN obj's underlying member method code with example usage: obj.recommend_k_items(test, top_k, sort_top_k, remove_seen, use_id) and returns: df