code: def test(loader):
      model.eval()
  
      correct = 0
      for data in loader:
          data = data.to(device)
          with torch.no_grad():
              pred = model(data).max(dim=1)[1]
          correct += pred.eq(data.y).sum().item()
      return correct / len(loader.dataset)
  
file_path: pytorch_geometric/examples/dgcnn_classification.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import ModelNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, DynamicEdgeConv, global_max_pool
label_desc: Use image node_id 2 for calling a global function with example usage: test(loader) and returns: unknown