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: Export user and item embeddings to csv files.
  
  Args:
      user_file (str): Path of file to save user embeddings.
      item_file (str): Path of file to save item embeddings.
docstr_tok: ["Export","user","and","item","embeddings","to","csv","files",".","Args",":","user_file","(","str",")",":","Path","of","file","to","save","user","embeddings",".","item_file","(","str",")",":","Path","of","file","to","save","item","embeddings","."]
code: def infer_embedding(self, user_file, item_file):
      """Export user and item embeddings to csv files.
  
      Args:
          user_file (str): Path of file to save user embeddings.
          item_file (str): Path of file to save item embeddings.
  
      """
      # create output directories if they do not exist
      dirs, _ = os.path.split(user_file)
      if not os.path.exists(dirs):
          os.makedirs(dirs)
      dirs, _ = os.path.split(item_file)
      if not os.path.exists(dirs):
          os.makedirs(dirs)
  
      data = self.data
  
      self.output_embeddings(
          data.id2user, self.n_users, self.ua_embeddings, user_file
      )
      self.output_embeddings(
          data.id2item, self.n_items, self.ia_embeddings, item_file
      )
  
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 12 for calling the LightGCN obj's underlying member method code with example usage: obj.infer_embedding(user_file, item_file) without return types