code: def weights_init(self, m):
      if isinstance(m, GCNConv):
          torch.nn.init.xavier_uniform_(m.weight.data)
          if m.bias is not None:
              m.bias.data.fill_(0.0)
  
imports: from functools import partial
  import torch
  import torch.nn.functional
  from torch.nn import Parameter
  from torch.nn import Sequential, Linear, BatchNorm1d
  from torch_scatter import scatter_add
  from torch_geometric.nn import GINConv, GCNConv, global_add_pool, global_mean_pool
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.utils import remove_self_loops, add_self_loops
  from torch_geometric.nn.inits import glorot, zeros
label_desc: Use image node_id 2 for calling the GCN obj's underlying member method code with example usage: obj.weights_init(m) without return types