code: def forward(self, x0, edge_index, edge_weight=None):
      x1 = F.relu(self.conv1(x0, edge_index, edge_weight))
      x1 = F.dropout(x1, p=0.2, training=self.training)
      x2 = F.relu(self.conv2(x1, edge_index, edge_weight))
      x2 = F.dropout(x2, p=0.2, training=self.training)
      x3 = F.relu(self.conv3(x2, edge_index, edge_weight))
      x3 = F.dropout(x3, p=0.2, training=self.training)
      x = torch.cat([x1, x2, x3], dim=-1)
      x = self.lin(x)
      return x.log_softmax(dim=-1)
  
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 3 for calling the Net obj's underlying member method code with example usage: obj.forward(x0, edge_index, edge_weight) and returns: x