class_docstr: LightGCN is a GCN-based recommender model.
  
  LightGCN includes only the most essential component in GCN — neighborhood aggregation — for
  collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly
  propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings
  learned at all layers as the final embedding.
  
  We implement the model following the original author with a pairwise training mode.
docstr: Get the embedding of users and items and combine to an embedding matrix.
  
  Returns:
      Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim]
docstr_tok: ["Get","the","embedding","of","users","and","items","and","combine","to","an","embedding","matrix",".","Returns",":","Tensor","of","the","embedding","matrix",".","Shape","of","[","n_items+n_users",",","embedding_dim","]"]
code: def get_ego_embeddings(self):
      r"""Get the embedding of users and items and combine to an embedding matrix.
  
      Returns:
          Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim]
      """
      user_embeddings = self.user_embedding.weight
      item_embeddings = self.item_embedding.weight
      ego_embeddings = torch.cat(
          [user_embeddings, item_embeddings], dim=0
      )
      return ego_embeddings
  
file_path: RecBole/recbole/model/general_recommender/lightgcn.py
imports: import numpy
  import scipy.sparse
  import torch
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_uniform_initialization
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 3 for calling the LightGCN obj's underlying member method code with example usage: obj.get_ego_embeddings() and returns: ego_embeddings