code: def inference(
      self,
      x_all: Tensor,
      device: torch.device,
      subgraph_loader: NeighborLoader,
  ) -> Tensor:
      pbar = tqdm(total=len(subgraph_loader) * len(self.convs))
      pbar.set_description("Evaluating")
  
      # Compute representations of nodes layer by layer, using *all*
      # available edges. This leads to faster computation in contrast to
      # immediately computing the final representations of each batch:
      for i, conv in enumerate(self.convs):
          xs = []
          for batch in subgraph_loader:
              x = x_all[batch.node_id.to(x_all.device)].to(device)
              x = conv(x, batch.edge_index.to(device))
              x = x[: batch.batch_size]
              if i < len(self.convs) - 1:
                  x = x.relu_()
              xs.append(x.cpu())
              pbar.update(1)
          x_all = torch.cat(xs, dim=0)
  
      pbar.close()
      return x_all
  
file_path: pytorch_geometric/examples/multi_gpu/distributed_sampling_multinode.py
imports: import copy
  import os
  import torch
  import torch.distributed
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import NeighborLoader
  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