code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout=0.0,
      normalize=False,
      train_eps=False,
  ):
      super(GNN, self).__init__()
  
      self.num_layers = num_layers
      self.dropout = dropout
      self.normalize = normalize
      self.train_eps = train_eps
  
      self.convs = torch.nn.ModuleList()
      self.lins_l = torch.nn.ModuleList()
      self.lins_r = torch.nn.ModuleList()
  
      self.convs.append(Conv(eps=0.0, train_eps=self.train_eps))
      self.lins_l.append(
          Linear(in_channels, hidden_channels, bias=True)
      )
      self.lins_r.append(
          Linear(in_channels, hidden_channels, bias=False)
      )
      for _ in range(self.num_layers - 2):
          self.convs.append(Conv(eps=0.0, train_eps=self.train_eps))
          self.lins_l.append(
              Linear(hidden_channels, hidden_channels, bias=True)
          )
          self.lins_r.append(
              Linear(hidden_channels, hidden_channels, bias=False)
          )
      self.convs.append(Conv(eps=0.0, train_eps=self.train_eps))
      self.lins_l.append(
          Linear(hidden_channels, out_channels, bias=True)
      )
      self.lins_r.append(
          Linear(hidden_channels, out_channels, bias=False)
      )
  
imports: import argparse
  import os.path
  import time
  import torch
  import torch.nn.functional
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit, Reddit2
  from torch_geometric.nn.conv import MessagePassing
  from torch.nn import Linear
  from torch import Tensor
  from logger import Logger
  from torch_geometric.data import NeighborSampler
label_desc: Use image node_id 1 to create a new GNN object from inherited base classes: torch.nn with example: obj = GNN(in_channels, hidden_channels, out_channels, num_layers, dropout, normalize, train_eps)