code: def __init__(self):
      super().__init__()
      if WITH_TORCH_SPLINE_CONV:
          self.conv1 = SplineConv(
              dataset.num_features, 32, dim=2, kernel_size=5
          )
          self.conv2 = SplineConv(32, 64, dim=2, kernel_size=5)
      else:
          nn1 = Sequential(
              Linear(2, 25),
              ReLU(),
              Linear(25, dataset.num_features * 32),
          )
          self.conv1 = NNConv(
              dataset.num_features, 32, nn1, aggr="mean"
          )
  
          nn2 = Sequential(Linear(2, 25), ReLU(), Linear(25, 32 * 64))
          self.conv2 = NNConv(32, 64, nn2, aggr="mean")
  
      self.lin1 = torch.nn.Linear(64, 128)
      self.lin2 = torch.nn.Linear(128, dataset.num_classes)
  
file_path: pytorch_geometric/examples/multi_gpu/data_parallel.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear, ReLU, Sequential
  import torch_geometric.transforms
  from torch_geometric.datasets import MNISTSuperpixels
  from torch_geometric.loader import DataListLoader
  from torch_geometric.nn import DataParallel, NNConv, SplineConv, global_mean_pool
  from torch_geometric.typing import WITH_TORCH_SPLINE_CONV
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net()