class_docstr: DGCF is a disentangled representation enhanced matrix factorization model.
  The interaction matrix of :math:`n_{users} \times n_{items}` is decomposed to :math:`n_{factors}` intent graph,
  we carefully design the data interface and use sparse tensor to train and test efficiently.
  We implement the model following the original author with a pairwise training mode.
code: def _get_ego_embeddings(self):
      # concat of user embeddings and item embeddings
      user_emb = self.user_embedding.weight
      item_emb = self.item_embedding.weight
      ego_embeddings = torch.cat([user_emb, item_emb], dim=0)
      return ego_embeddings
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 3 for calling the DGCF obj's underlying member method code with example usage: obj._get_ego_embeddings() and returns: ego_embeddings