code: def test() -> List[float]:
      model.eval()
      pred = model(data.x_dict, data.edge_index_dict).argmax(dim=-1)
  
      accs = []
      for split in ["train_mask", "val_mask", "test_mask"]:
          mask = data["movie"][split]
          acc = (pred[mask] == data["movie"].y[mask]).sum() / mask.sum()
          accs.append(float(acc))
      return accs
  
file_path: pytorch_geometric/examples/hetero/han_imdb.py
imports: import os.path
  from typing import Dict, List, Union
  import torch
  import torch.nn.functional
  from torch import nn
  import torch_geometric.transforms
  from torch_geometric.datasets import IMDB
  from torch_geometric.nn import HANConv
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: accs