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 _build_sparse_tensor(self, indices, values, size):
      # Construct the sparse matrix with indices, values and size.
      return torch.sparse.FloatTensor(indices, values, size).to(
          self.device
      )
  
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 2 for calling the DGCF obj's underlying member method code with example usage: obj._build_sparse_tensor(indices, values, size) and returns: torch