code: def test(self, x_all, subgraph_loader):
      for i in range(self.num_layers):
          xs = []
          for batch_size, n_id, adj in subgraph_loader:
              edge_index, _, size = adj
              x = x_all[n_id.to(x_all.device)].to(
                  train.torch.get_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())
  
          x_all = torch.cat(xs, dim=0)
  
      return x_all
  
imports: import argparse
  import os
  import torch
  import torch.nn.functional
  from filelock import FileLock
  from torch_geometric.datasets import FakeDataset, Reddit
  from torch_geometric.loader import NeighborSampler
  from torch_geometric.nn import SAGEConv
  from torch_geometric.transforms import RandomNodeSplit
  from ray import train
  from ray.train import ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 3 for calling the SAGE obj's underlying member method code with example usage: obj.test(x_all, subgraph_loader) and returns: x_all