code: def test(loader):
      model.eval()
  
      correct = 0
      for data in loader:
          data = data.to(device)
          out = model(data.x, data.pos, data.batch, data.ptr)
          correct += int((out.argmax(dim=-1) == data.y).sum())
      return correct / len(loader.dataset)
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Linear
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import ModelNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP
  from torch_geometric.nn.aggr import MaxAggregation
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.pool import knn_graph
  from torch_geometric.nn.pool.decimation import decimation_indices
  from torch_geometric.typing import WITH_TORCH_CLUSTER
  from torch_geometric.utils import softmax
label_desc: Use image node_id 3 for calling a global function with example usage: test(loader) and returns: unknown