code: def test():
      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["author"][split]
          acc = (
              pred[mask] == data["author"].y[mask]
          ).sum() / mask.sum()
          accs.append(float(acc))
      return accs
  
file_path: pytorch_geometric/examples/hetero/hgt_dblp.py
imports: import os.path
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.datasets import DBLP
  from torch_geometric.nn import HGTConv, Linear
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: accs