code: def __init__(self, in_features, out_features, bias=False):
      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()
  
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 1 to create a new GraphConvolution object from inherited base classes: nn with example: obj = GraphConvolution(in_features, out_features, bias)