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 forward(self, item_seq, item_seq_len):
      assert 0 <= self.weight <= 1
      alias_inputs, A, items = self._get_slice(item_seq)
      hidden = self.item_embedding(items)
      hidden = self.gnn(A, hidden)
      alias_inputs = alias_inputs.view(
          -1, alias_inputs.size(1), 1
      ).expand(-1, -1, self.hidden_size)
      seq_hidden = torch.gather(hidden, dim=1, index=alias_inputs)
      # fetch the last hidden state of last timestamp
      ht = self.gather_indexes(seq_hidden, item_seq_len - 1)
      a = seq_hidden
      attention_mask = self.get_attention_mask(item_seq)
  
      outputs = self.self_attention(
          a, attention_mask, output_all_encoded_layers=True
      )
      output = outputs[-1]
      at = self.gather_indexes(output, item_seq_len - 1)
      seq_output = self.weight * at + (1 - self.weight) * ht
      return seq_output
  
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 4 for calling the GCSAN obj's underlying member method code with example usage: obj.forward(item_seq, item_seq_len) and returns: seq_output