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.
docstr: Calculate the correlation loss for a sampled users and items.
  
  Args:
      cor_u_embeddings (torch.cuda.FloatTensor): (cor_batch_size, n_factors)
      cor_i_embeddings (torch.cuda.FloatTensor): (cor_batch_size, n_factors)
  
  Returns:
      torch.Tensor : correlation loss.
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code: def create_cor_loss(self, cor_u_embeddings, cor_i_embeddings):
      r"""Calculate the correlation loss for a sampled users and items.
  
      Args:
          cor_u_embeddings (torch.cuda.FloatTensor): (cor_batch_size, n_factors)
          cor_i_embeddings (torch.cuda.FloatTensor): (cor_batch_size, n_factors)
  
      Returns:
          torch.Tensor : correlation loss.
      """
      cor_loss = None
  
      ui_embeddings = torch.cat(
          (cor_u_embeddings, cor_i_embeddings), dim=0
      )
      ui_factor_embeddings = torch.chunk(
          ui_embeddings, self.n_factors, 1
      )
  
      for i in range(0, self.n_factors - 1):
          x = ui_factor_embeddings[i]
          # (M + N, emb_size / n_factor)
          y = ui_factor_embeddings[i + 1]
          # (M + N, emb_size / n_factor)
          if i == 0:
              cor_loss = self._create_distance_correlation(x, y)
          else:
              cor_loss += self._create_distance_correlation(x, y)
  
      cor_loss /= (self.n_factors + 1.0) * self.n_factors / 2
  
      return cor_loss
  
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 7 for calling the DGCF obj's underlying member method code with example usage: obj.create_cor_loss(cor_u_embeddings, cor_i_embeddings) and returns: cor_loss