class_docstr: Graph Convolutional Network
class_docstr_tok: ["Graph","Convolutional","Network"]
code: def _build_graph_separately(self, entity_emb):
      # node dropout
      if self.node_dropout_rate > 0.0:
          edge_index, edge_type = self.edge_sampling(
              self.edge_index, self.edge_type, self.node_dropout_rate
          )
          inter_matrix = self.node_dropout(self.inter_matrix)
      else:
          edge_index, edge_type = self.edge_index, self.edge_type
          inter_matrix = self.inter_matrix
  
      origin_item_adj = self.build_adj(entity_emb, self.topk)
  
      entity_res_emb = [entity_emb]  # [n_entities, embedding_size]
      relation_emb = (
          self.relation_embedding.weight
      )  # [n_relations, embedding_size]
      for i in range(len(self.bg_convs)):
          entity_emb = self.bg_convs[i](
              entity_emb,
              None,
              relation_emb,
              edge_index,
              edge_type,
              inter_matrix,
          )
          # message dropout
          if self.mess_dropout_rate > 0.0:
              entity_emb = self.mess_dropout(entity_emb)
          entity_emb = F.normalize(entity_emb)
          # result embedding
          entity_res_emb.append(entity_emb)
  
      entity_res_emb = torch.stack(entity_res_emb, dim=1)
      entity_res_emb = entity_res_emb.mean(dim=1, keepdim=False)
  
      item_adj = (1 - self.lambda_coeff) * self.build_adj(
          entity_res_emb, self.topk
      ) + self.lambda_coeff * origin_item_adj
  
      return item_adj
  
imports: import numpy
  import scipy.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.layers import SparseDropout
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 5 for calling the GraphConv obj's underlying member method code with example usage: obj._build_graph_separately(entity_emb) and returns: item_adj