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.
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.loss import BPRLoss
label_desc: Use image node_id 3 for calling the GNN obj's underlying member method code with example usage: obj.forward(A, hidden) and returns: hidden