code: def forward(self, input, adj=None, relu=False):
      support = torch.matmul(input, self.weight)
      # print(support.size(),adj.size())
      if adj is not None:
          output = torch.matmul(adj, support)
      else:
          output = support
      # print(output.size())
      if self.bias is not None:
          return output + self.bias
      else:
          if relu:
              return F.relu(output)
          else:
              return output
  
imports: import torch
  from .None import graph
  import torch.nn
  import torch.nn.functional
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 3 for calling the GraphConvolution obj's underlying member method code with example usage: obj.forward(input, adj, relu) and returns: unknown, F, output