code: def test(loader):
      model.eval()
  
      ys, preds = [], []
      for data in loader:
          ys.append(data.y)
          with torch.no_grad():
              out = model(data.x.to(device), data.edge_index.to(device))
          preds.append((out > 0).float().cpu())
  
      y, pred = (
          torch.cat(ys, dim=0).numpy(),
          torch.cat(preds, dim=0).numpy(),
      )
      return f1_score(y, pred, average="micro") if pred.sum() > 0 else 0
  
imports: import argparse
  import os.path
  import time
  import torch
  from sklearn.metrics import f1_score
  from torch_geometric.datasets import PPI
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GATConv
label_desc: Use image node_id 2 for calling a global function with example usage: test(loader) and returns: unknown