code: def eval(model, device, loader, evaluator):
      model.eval()
      y_true = []
      y_pred = []
  
      for step, batch in enumerate(tqdm(loader, desc="Iteration")):
          x, y = batch
          x = x.to(device).to(torch.float32)
          y = y.to(device)
  
          with torch.no_grad():
              pred = model(x).view(
                  -1,
              )
  
          y_true.append(y.view(pred.shape).detach().cpu())
          y_pred.append(pred.detach().cpu())
  
      y_true = torch.cat(y_true, dim=0)
      y_pred = torch.cat(y_pred, dim=0)
  
      input_dict = {"y_true": y_true, "y_pred": y_pred}
  
      return evaluator.eval(input_dict)["mae"]
  
imports: import torch
  from torch.utils.data import DataLoader, TensorDataset
  import torch.optim
  import torch.nn.functional
  from torch.utils.tensorboard import SummaryWriter
  from torch.optim.lr_scheduler import StepLR
  import os
  import os.path
  from tqdm.auto import tqdm
  import argparse
  import time
  import numpy
  import random
  from rdkit import Chem
  from rdkit.Chem import AllChem
  from ogb.lsc import PCQM4Mv2Dataset, PCQM4Mv2Evaluator
label_desc: Use image node_id 2 for calling a global function with example usage: eval(model, device, loader, evaluator) and returns: evaluator