code: def forward(self, x, edge_index, edge_weight=None):
      x = torch.matmul(x, self.weight)
      if self.gfn:
          return x
  
      if not self.cached or self.cached_result is None:
          if self.edge_norm:
              edge_index, norm = ResGCNConv.norm(
                  edge_index,
                  x.size(0),
                  edge_weight,
                  self.improved,
                  x.dtype,
              )
          else:
              norm = None
          self.cached_result = edge_index, norm
  
      edge_index, norm = self.cached_result
      return self.propagate(edge_index, x=x, norm=norm)
  
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 4 for calling the ResGCNConv obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_weight) and returns: self, x