code: def inference(self, x_all):
      pbar = tqdm(total=x_all.size(0) * self.num_layers, disable=True)
      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 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 self.normalize:
                  x = F.normalize(x, p=2.0, dim=-1)
              x = self.lins_l[i](x) + self.lins_r[i](x_target)
              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
  
imports: import argparse
  import os.path
  import time
  import torch
  import torch.nn.functional
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit, Reddit2
  from torch_geometric.nn.conv import MessagePassing
  from torch.nn import Linear
  from torch import Tensor
  from logger import Logger
  from torch_geometric.data import NeighborSampler
label_desc: Use image node_id 4 for calling the GNN obj's underlying member method code with example usage: obj.inference(x_all) and returns: x_all