code: def __init__(self, input_dim, hidden_dim, output_dim, args):
      super(GNN, self).__init__()
      self.dropout = args.dropout
      self.num_layers = args.num_layers
  
      conv_model = self.build_conv_model(args.model)
      self.convs = nn.ModuleList()
      self.convs.append(conv_model(input_dim, hidden_dim))
  
      for l in range(args.num_layers - 2):
          self.convs.append(conv_model(hidden_dim, hidden_dim))
      self.convs.append(conv_model(hidden_dim, output_dim))
  
imports: import torch
  import argparse
  import numpy
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  import torch_geometric.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from sklearn.metrics import *
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 1 to create a new GNN object from inherited base classes: torch.nn with example: obj = GNN(input_dim, hidden_dim, output_dim, args)