code: def __init__(self):
      super().__init__()
      nn1 = Sequential(
          Linear(2, 25),
          ReLU(),
          Linear(25, d.num_features * 32),
      )
      self.conv1 = NNConv(d.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.fc1 = torch.nn.Linear(64, 128)
      self.fc2 = torch.nn.Linear(128, d.num_classes)
  
file_path: pytorch_geometric/examples/contrib/pgm_explainer_graph_classification.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.contrib.explain import PGMExplainer
  from torch_geometric.datasets import MNISTSuperpixels
  from torch_geometric.explain import Explainer
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import NNConv, global_mean_pool, graclus, max_pool, max_pool_x
  from torch_geometric.utils import normalized_cut
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net()