code: def __init__(self, in_channels, hidden_channels, out_channels):
      super().__init__()
      # Initialize edge weights of self-loops with 1:
      self.conv1 = WeightedGATConv(
          in_channels, hidden_channels, fill_value=1.0
      )
      self.conv2 = WeightedGATConv(
          hidden_channels, out_channels, fill_value=1.0
      )
  
imports: import copy
  import os.path
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.optim import Adam
  import torch_geometric.transforms
  from torch_geometric.contrib.nn import GRBCDAttack, PRBCDAttack
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import GATConv, GCNConv
  from torch_geometric.nn.conv.gcn_conv import gcn_norm
  from torch_geometric.utils import softmax
label_desc: Use image node_id 1 to create a new GAT object from inherited base classes: torch.nn with example: obj = GAT(in_channels, hidden_channels, out_channels)