code: def test(model, data, split_idx, evaluator):
      model.eval()
  
      out = model(data.x, data.adj_t)
      y_pred = out.argmax(dim=-1, keepdim=True)
  
      train_acc = evaluator.eval(
          {
              "y_true": data.y[split_idx["train"]["paper"]],
              "y_pred": y_pred[split_idx["train"]["paper"]],
          }
      )["acc"]
      valid_acc = evaluator.eval(
          {
              "y_true": data.y[split_idx["valid"]["paper"]],
              "y_pred": y_pred[split_idx["valid"]["paper"]],
          }
      )["acc"]
      test_acc = evaluator.eval(
          {
              "y_true": data.y[split_idx["test"]["paper"]],
              "y_pred": y_pred[split_idx["test"]["paper"]],
          }
      )["acc"]
  
      return train_acc, valid_acc, test_acc
  
imports: import argparse
  import torch
  import torch.nn.functional
  from torch_geometric.data import Data
  import torch_geometric.transforms
  from torch_geometric.nn import GCNConv, SAGEConv
  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, data, split_idx, evaluator) and returns: train_acc,  valid_acc,  test_acc