code: def sample_cor_samples(n_users, n_items, cor_batch_size):
      r"""This is a function that sample item ids and user ids.
  
      Args:
          n_users (int): number of users in total
          n_items (int): number of items in total
          cor_batch_size (int): number of id to sample
  
      Returns:
          list: cor_users, cor_items. The result sampled ids with both as cor_batch_size long.
  
      Note:
          We have to sample some embedded representations out of all nodes.
          Because we have no way to store cor-distance for each pair.
      """
      cor_users = rd.sample(list(range(n_users)), cor_batch_size)
      cor_items = rd.sample(list(range(n_items)), cor_batch_size)
  
      return cor_users, cor_items
  
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 1 for calling a global function with example usage: sample_cor_samples(n_users, n_items, cor_batch_size) and returns: cor_users,  cor_items