code: def forward(self, x, adjs):
      for i, (edge_index, _, size) in enumerate(adjs):
          x_target = x[
              : size[1]
          ]  # Target nodes are always placed first.
          x = self.convs[i]((x, x_target), edge_index)
          if i != self.num_layers - 1:
              x = F.relu(x)
              x = F.dropout(x, p=0.5, training=self.training)
      return x.log_softmax(dim=-1)
  
file_path: pytorch_geometric/examples/quiver/multi_gpu_quiver.py
imports: import os
  import quiver
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import NeighborSampler
  from torch_geometric.nn import SAGEConv
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.forward(x, adjs) and returns: x