code: def inference(self, x_all):
      pbar = tqdm(total=x_all.size(0) * 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:
              edge_index = batch.edge_index.to(device)
              x = x_all[batch.n_id].to(device)
              x_target = x[: batch.batch_size]
              x = conv((x, x_target), edge_index)
              if i != len(self.convs) - 1:
                  x = F.relu(x)
              xs.append(x.cpu())
  
              pbar.update(batch.batch_size)
  
          x_all = torch.cat(xs, dim=0)
  
      pbar.close()
  
      return x_all
  
file_path: pytorch_geometric/examples/cluster_gcn_reddit.py
imports: import time
  import torch
  import torch.nn.functional
  from torch.nn import ModuleList
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import ClusterData, ClusterLoader, NeighborLoader
  from torch_geometric.nn import SAGEConv
label_desc: Use image node_id 3 for calling the Net obj's underlying member method code with example usage: obj.inference(x_all) and returns: x_all