class_docstr: GCSAN captures rich local dependencies via graph neural network,
   and learns long-range dependencies by applying the self-attention mechanism.
  
  Note:
  
      In the original paper, the attention mechanism in the self-attention layer is a single head,
      for the reusability of the project code, we use a unified transformer component.
      According to the experimental results, we only applied regularization to embedding.
class_docstr_tok: ["GCSAN","captures","rich","local","dependencies","via","graph","neural","network",",","and","learns","long-range","dependencies","by","applying","the","self-attention","mechanism",".","Note",":","In","the","original","paper",",","the","attention","mechanism","in","the","self-attention","layer","is","a","single","head",",","for","the","reusability","of","the","project","code",",","we","use","a","unified","transformer","component",".","According","to","the","experimental","results",",","we","only","applied","regularization","to","embedding","."]
code: def _get_slice(self, item_seq):
      items, n_node, A, alias_inputs = [], [], [], []
      max_n_node = item_seq.size(1)
      item_seq = item_seq.cpu().numpy()
  
      for u_input in item_seq:
          node = np.unique(u_input)
          items.append(node.tolist() + (max_n_node - len(node)) * [0])
          u_A = np.zeros((max_n_node, max_n_node))
          for i in np.arange(len(u_input) - 1):
              if u_input[i + 1] == 0:
                  break
              u = np.where(node == u_input[i])[0][0]
              v = np.where(node == u_input[i + 1])[0][0]
              u_A[u][v] = 1
          u_sum_in = np.sum(u_A, 0)
          u_sum_in[np.where(u_sum_in == 0)] = 1
          u_A_in = np.divide(u_A, u_sum_in)
          u_sum_out = np.sum(u_A, 1)
          u_sum_out[np.where(u_sum_out == 0)] = 1
          u_A_out = np.divide(u_A.transpose(), u_sum_out)
          u_A = np.concatenate([u_A_in, u_A_out]).transpose()
          A.append(u_A)
  
          alias_inputs.append(
              [np.where(node == i)[0][0] for i in u_input]
          )
      # The relative coordinates of the item node, shape of [batch_size, max_session_len]
      alias_inputs = torch.LongTensor(alias_inputs).to(self.device)
      # The connecting matrix, shape of [batch_size, max_session_len, 2 * max_session_len]
      A = torch.FloatTensor(np.array(A)).to(self.device)
      # The unique item nodes, shape of [batch_size, max_session_len]
      items = torch.LongTensor(items).to(self.device)
  
      return alias_inputs, A, items
  
imports: import math
  import numpy
  import torch
  from torch import nn
  from torch.nn import Parameter
  from torch.nn import functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
  from recbole.model.loss import EmbLoss, BPRLoss
label_desc: Use image node_id 3 for calling the GCSAN obj's underlying member method code with example usage: obj._get_slice(item_seq) and returns: alias_inputs,  A,  items