code: def inference(self, x_all, device, subgraph_loader):
      pbar = tqdm(total=x_all.size(0) * self.num_layers)
      pbar.set_description("Evaluating")
  
      for i in range(self.num_layers):
          xs = []
          for batch_size, n_id, adj in subgraph_loader:
              edge_index, _, size = adj.to(device)
              x = x_all[n_id].to(device)
              x_target = x[: size[1]]
              x = self.convs[i]((x, x_target), edge_index)
              if i != self.num_layers - 1:
                  x = F.relu(x)
              xs.append(x.cpu())
  
              pbar.update(batch_size)
  
          x_all = torch.cat(xs, dim=0)
  
      pbar.close()
  
      return x_all
  
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 3 for calling the SAGE obj's underlying member method code with example usage: obj.inference(x_all, device, subgraph_loader) and returns: x_all