code: def __init__(self, hidden_channels):
      super().__init__()
      in_channels = dataset.num_node_features
      out_channels = dataset.num_classes
      self.conv1 = GraphConv(in_channels, hidden_channels)
      self.conv2 = GraphConv(hidden_channels, hidden_channels)
      self.conv3 = GraphConv(hidden_channels, hidden_channels)
      self.lin = torch.nn.Linear(3 * hidden_channels, out_channels)
  
file_path: pytorch_geometric/examples/graph_saint.py
imports: import argparse
  import os.path
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Flickr
  from torch_geometric.loader import GraphSAINTRandomWalkSampler
  from torch_geometric.nn import GraphConv
  from torch_geometric.typing import WITH_TORCH_SPARSE
  from torch_geometric.utils import degree
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net(hidden_channels)