code: def test(model, x, y_true, split_idx, evaluator):
      model.eval()
  
      out = model(x)
      y_pred = out.argmax(dim=-1, keepdim=True)
  
      train_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["train"]],
              "y_pred": y_pred[split_idx["train"]],
          }
      )["acc"]
      valid_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["valid"]],
              "y_pred": y_pred[split_idx["valid"]],
          }
      )["acc"]
      test_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["test"]],
              "y_pred": y_pred[split_idx["test"]],
          }
      )["acc"]
  
      return train_acc, valid_acc, test_acc
  
file_path: ogb/examples/nodeproppred/products/mlp.py
imports: import argparse
  import torch
  import torch.nn.functional
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 2 for calling a global function with example usage: test(model, x, y_true, split_idx, evaluator) and returns: train_acc,  valid_acc,  test_acc