code: def forward(self, x, edge_index, edge_weight=None):
      for conv in self.convs[:-1]:
          x = conv(x, edge_index, edge_weight)
          x = F.relu(x)
          x = F.dropout(x, p=self.dropout, training=self.training)
      x = self.convs[-1](x, edge_index, edge_weight)
      return torch.log_softmax(x, dim=-1)
  
file_path: ogb/examples/nodeproppred/products/graph_saint.py
imports: import argparse
  import torch
  from tqdm.auto import tqdm
  import torch.nn.functional
  from torch_geometric.data import GraphSAINTRandomWalkSampler, NeighborSampler
  from torch_geometric.nn import SAGEConv
  from torch_geometric.utils import subgraph
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 3 for calling the SAGE obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_weight) and returns: torch