code: def evaluate(
      args,
      model,
      dataloader,
      labels,
      train_idx,
      val_idx,
      test_idx,
      criterion,
      evaluator,
  ):
      model.eval()
  
      preds = torch.zeros(labels.shape).to(device)
  
      # Due to the memory capacity constraints, we use sampling for inference and calculate the average of the predictions 'eval_times' times.
      eval_times = 1
  
      for _ in range(eval_times):
          for input_nodes, output_nodes, subgraphs in dataloader:
              subgraphs = [b.to(device) for b in subgraphs]
              new_train_idx = list(range(len(input_nodes)))
  
              if args.use_labels:
                  add_labels(subgraphs[0], new_train_idx)
  
              pred = model(subgraphs)
              preds[output_nodes] += pred
  
              # torch.cuda.empty_cache()
  
      preds /= eval_times
  
      train_loss = criterion(
          preds[train_idx], labels[train_idx].float()
      ).item()
      val_loss = criterion(
          preds[val_idx], labels[val_idx].float()
      ).item()
      test_loss = criterion(
          preds[test_idx], labels[test_idx].float()
      ).item()
  
      return (
          evaluator(preds[train_idx], labels[train_idx]),
          evaluator(preds[val_idx], labels[val_idx]),
          evaluator(preds[test_idx], labels[test_idx]),
          train_loss,
          val_loss,
          test_loss,
          preds,
      )
  
imports: import argparse
  import os
  import random
  import sys
  import time
  import dgl
  import dgl.function
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.nn.functional
  import torch.optim
  from dgl.dataloading import DataLoader, MultiLayerFullNeighborSampler, MultiLayerNeighborSampler
  from matplotlib.ticker import AutoMinorLocator, MultipleLocator
  from models import GAT
  from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
  from torch import nn
label_desc: Use image node_id 7 for calling a global function with example usage: evaluate(args, model, dataloader, labels, train_idx, val_idx, test_idx, criterion, evaluator) and returns: evaluator,  evaluator,  evaluator,  train_loss,  val_loss,  test_loss,  preds