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: Initialize user and item embeddings.
  
  Returns:
      dict: With keys `user_embedding` and `item_embedding`, embeddings of all users and items.
docstr_tok: ["Initialize","user","and","item","embeddings",".","Returns",":","dict",":","With","keys","`","user_embedding","`","and","`","item_embedding","`",",","embeddings","of","all","users","and","items","."]
code: def _init_weights(self):
      """Initialize user and item embeddings.
  
      Returns:
          dict: With keys `user_embedding` and `item_embedding`, embeddings of all users and items.
  
      """
      all_weights = dict()
      initializer = tf.compat.v1.keras.initializers.VarianceScaling(
          scale=1.0, mode="fan_avg", distribution="uniform"
      )
  
      all_weights["user_embedding"] = tf.Variable(
          initializer([self.n_users, self.emb_dim]),
          name="user_embedding",
      )
      all_weights["item_embedding"] = tf.Variable(
          initializer([self.n_items, self.emb_dim]),
          name="item_embedding",
      )
      print("Using xavier initialization.")
  
      return all_weights
  
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 2 for calling the LightGCN obj's underlying member method code with example usage: obj._init_weights() and returns: all_weights