text: from SAGE.layers import SageConv
  import torch.nn.functional as F
  import torch.nn as nn
  
  class Sage(nn.Module):
      def __init__(self, nfeat, nhid, nclass, dropout):
          super(Sage, self).__init__()
  
          self.sage1 = SageConv(nfeat, nhid)
          self.mlp = nn.Linear(nhid, nclass)
          self.dropout = dropout
  
          self.reset_parameters()
  
      def reset_parameters(self):
          nn.init.normal_(self.mlp.weight,std=0.05)
  
      def forward(self, x, adj):
          x = F.relu(self.sage1(x, adj))
          x = F.dropout(x, self.dropout, training=self.training)
          x = self.mlp(x)
  
          return x