code: def __init__(
      self,
      feat_dim,
      hidden_dim,
      n_layers=3,
      pool="sum",
      bn=False,
      act="relu",
      bias=True,
      xavier=True,
      edge_weight=False,
  ):
      super(GCN, self).__init__()
  
      if bn:
          self.bns = torch.nn.ModuleList()
      else:
          self.bns = None
      self.convs = torch.nn.ModuleList()
      self.acts = torch.nn.ModuleList()
      self.n_layers = n_layers
      self.pool = pool
      self.edge_weight = edge_weight
      self.normalize = not edge_weight
      self.add_self_loops = not edge_weight
  
      if act == "prelu":
          a = torch.nn.PReLU()
      else:
          a = torch.nn.ReLU()
  
      for i in range(n_layers):
          start_dim = hidden_dim if i else feat_dim
          conv = GCNConv(
              start_dim,
              hidden_dim,
              bias=bias,
              add_self_loops=self.add_self_loops,
              normalize=self.normalize,
          )
          if xavier:
              self.weights_init(conv.lin)
          self.convs.append(conv)
          self.acts.append(a)
          if bn:
              self.bns.append(BatchNorm1d(hidden_dim))
  
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 1 to create a new GCN object from inherited base classes: torch.nn with example: obj = GCN(feat_dim, hidden_dim, n_layers, pool, bn, act, bias, xavier, edge_weight)