code: def test(loader):
      model.eval()
  
      total_examples = total_correct = 0
      for batch in tqdm(loader):
          batch = batch.to(device, "edge_index")
          batch_size = batch["paper"].batch_size
          out = model(batch.x_dict, batch.edge_index_dict)["paper"][
              :batch_size
          ]
          pred = out.argmax(dim=-1)
  
          total_examples += batch_size
          total_correct += int(
              (pred == batch["paper"].y[:batch_size]).sum()
          )
  
      return total_correct / total_examples
  
file_path: pytorch_geometric/examples/hetero/to_hetero_mag.py
imports: import argparse
  import os.path
  import torch
  import torch.nn.functional
  from torch.nn import ReLU
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import OGB_MAG
  from torch_geometric.loader import HGTLoader, NeighborLoader
  from torch_geometric.nn import Linear, SAGEConv, Sequential, to_hetero
label_desc: Use image node_id 3 for calling a global function with example usage: test(loader) and returns: unknown