code: def inference(self, x_all, subgraph_loader, device):
      pbar = tqdm(total=x_all.size(0) * len(self.convs))
      pbar.set_description("Evaluating")
  
      for i, conv in enumerate(self.convs):
          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 = conv((x, x_target), edge_index)
              if i != len(self.convs) - 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: ogb/examples/nodeproppred/products/cluster_gcn.py
imports: import argparse
  import torch
  from tqdm.auto import tqdm
  import torch.nn.functional
  from torch_geometric.data import ClusterData, ClusterLoader, NeighborSampler
  from torch_geometric.nn import SAGEConv
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 4 for calling the SAGE obj's underlying member method code with example usage: obj.inference(x_all, subgraph_loader, device) and returns: x_all