code: def test(loader):
      total_correct = total_examples = 0
      for batch in loader:
          batch = batch.to(device)
          pred = model(batch.x, batch.batch).sigmoid().round()
          num_mistakes = (pred != batch.y).sum(dim=-1)
          total_correct += int((num_mistakes == 0).sum())
          total_examples += batch.num_graphs
      return total_correct / total_examples
  
imports: import argparse
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import LCMAggregation
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 2 for calling a global function with example usage: test(loader) and returns: unknown