code: def forward(self, x, edge_index):
      x = F.dropout(x, p=self.dropout, training=self.training)
      xs = [x]
      for conv, norm in zip(self.convs, self.norms):
          x = conv(x, edge_index)
          x = norm(x)
          x = x.relu()
          x = F.dropout(x, p=self.dropout, training=self.training)
          xs.append(x)
      return self.mlp(torch.cat(xs, dim=-1))
  
file_path: pytorch_geometric/examples/kuzu/papers_100M/train.py
imports: import multiprocessing
  import os.path
  import kuzu
  import pandas
  import torch
  import torch.nn.functional
  from tqdm import tqdm
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import MLP, BatchNorm, SAGEConv
label_desc: Use image node_id 2 for calling the GraphSAGE obj's underlying member method code with example usage: obj.forward(x, edge_index) and returns: self