code: def __init__(
      self,
      feat_dim,
      hidden_dim,
      n_layers=3,
      pool="sum",
      bn=False,
      act="relu",
      bias=True,
      xavier=True,
  ):
      super(GIN, self).__init__()
  
      if bn:
          self.bns = torch.nn.ModuleList()
      else:
          self.bns = None
      self.convs = torch.nn.ModuleList()
      self.n_layers = n_layers
      self.pool = pool
  
      self.act = torch.nn.PReLU() if act == "prelu" else torch.nn.ReLU()
  
      for i in range(n_layers):
          start_dim = hidden_dim if i else feat_dim
          nn = Sequential(
              Linear(start_dim, hidden_dim, bias=bias),
              self.act,
              Linear(hidden_dim, hidden_dim, bias=bias),
          )
          if xavier:
              self.weights_init(nn)
          conv = GINConv(nn)
          self.convs.append(conv)
          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 GIN object from inherited base classes: torch.nn with example: obj = GIN(feat_dim, hidden_dim, n_layers, pool, bn, act, bias, xavier)