code: def mask_correlated_samples(self, batch_size):
      N = 2 * batch_size
      mask = torch.ones((N, N), dtype=bool)
      mask = mask.fill_diagonal_(0)
      for i in range(batch_size):
          mask[i, batch_size + i] = 0
          mask[batch_size + i, i] = 0
      return mask
  
imports: import random
  import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  import math
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.data.interaction import Interaction
label_desc: Use image node_id 11 for calling the FEARec obj's underlying member method code with example usage: obj.mask_correlated_samples(batch_size) and returns: mask