code: def test(self, U: Tensor, y_one_hot: Tensor, data: Data):
      self.eval()
      out = self(U)
  
      loss = F.mse_loss(
          out[data.trainval_mask],
          y_one_hot[data.trainval_mask],
      )
  
      accs = []
      pred = out.argmax(dim=-1)
      for _, mask in data("trainval_mask", "test_mask"):
          accs.append(
              float((pred[mask] == data.y[mask]).sum() / mask.sum())
          )
  
      return float(loss), accs[0], accs[1], pred
  
file_path: pytorch_geometric/examples/ogc.py
imports: import argparse
  import os.path
  import time
  import warnings
  import torch
  import torch.nn.functional
  from torch import Tensor
  import torch_geometric.transforms
  from torch_geometric.data import Data
  from torch_geometric.datasets import Planetoid
  from torch_geometric.utils import one_hot
label_desc: Use image node_id 3 for calling the LinearNeuralNetwork obj's underlying member method code with example usage: obj.test(U, y_one_hot, data) and returns: float,  accs,  accs,  pred