class_docstr: Graph neural networks are well-suited for session-based recommendation,
  because it can automatically extract features of session graphs with considerations of rich node connections.
class_docstr_tok: ["Graph","neural","networks","are","well-suited","for","session-based","recommendation",",","because","it","can","automatically","extract","features","of","session","graphs","with","considerations","of","rich","node","connections","."]
code: def forward(self, A, hidden):
      for i in range(self.step):
          hidden = self.GNNCell(A, hidden)
      return hidden
  
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 GNN obj's underlying member method code with example usage: obj.forward(A, hidden) and returns: hidden