text: import math
  
  import torch
  
  from torch.nn.parameter import Parameter
  from torch.nn.modules.module import Module
  
  
  class GraphConvolution(Module):
      """
      Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
      """
  
      def __init__(self, in_features, out_features, bias=True):
          super(GraphConvolution, self).__init__()
          self.in_features = in_features
          self.out_features = out_features
          self.weight = Parameter(torch.FloatTensor(in_features, out_features))
          if bias:
              self.bias = Parameter(torch.FloatTensor(out_features))
          else:
              self.register_parameter('bias', None)
          self.reset_parameters()
  
      def reset_parameters(self):
          stdv = 1. / math.sqrt(self.weight.size(1))
          self.weight.data.uniform_(-stdv, stdv)
          if self.bias is not None:
              self.bias.data.uniform_(-stdv, stdv)
  
      def forward(self, input, adj):
          support = torch.mm(input, self.weight)
          output = torch.spmm(adj, support)
          support1=torch.mm( adj,input)
          output2=torch.mm(support1, self.weight)
          if self.bias is not None:
              return output + self.bias
          else:
              return output
  
  
      def __repr__(self):
          return self.__class__.__name__ + ' (' \
                 + str(self.in_features) + ' -> ' \
                 + str(self.out_features) + ')'
  