code: def evaluate(dataloader, model, prog_args, logger=None):
      """
      evaluate function
      """
      if logger is not None and prog_args.save_dir is not None:
          model.load_state_dict(
              torch.load(
                  prog_args.save_dir
                  + "/"
                  + prog_args.dataset
                  + "/model.iter-"
                  + str(logger["best_epoch"])
              )
          )
      model.eval()
      correct_label = 0
      with torch.no_grad():
          for batch_idx, (batch_graph, graph_labels) in enumerate(
              dataloader
          ):
              for key, value in batch_graph.ndata.items():
                  batch_graph.ndata[key] = value.float()
              graph_labels = graph_labels.long()
              if torch.cuda.is_available():
                  batch_graph = batch_graph.to(
                      torch.cuda.current_device()
                  )
                  graph_labels = graph_labels.cuda()
              ypred = model(batch_graph)
              indi = torch.argmax(ypred, dim=1)
              correct = torch.sum(indi == graph_labels)
              correct_label += correct.item()
      result = correct_label / (len(dataloader) * prog_args.batch_size)
      return result
  
imports: import argparse
  import os
  import random
  import time
  import dgl
  import dgl.function
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.data
  from data_utils import pre_process
  from dgl import DGLGraph
  from dgl.data import tu
  from model.encoder import DiffPool
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(dataloader, model, prog_args, logger) and returns: result