code: def evaluate_one_dataset(
      LOG, dataloader, model, opt, save=True, give_return=True, epoch=0
  ):
      """
      Compute evaluation metrics, update LOGGER and print results.
  
      Args:
          LOG:         aux.LOGGER-instance. Main Logging Functionality.
          dataloader:  PyTorch Dataloader, Testdata to be evaluated.
          model:       PyTorch Network, Network to evaluate.
          opt:         argparse.Namespace, contains all training-specific parameters.
          save:        bool, if True, Checkpoints are saved when testing metrics (specifically Recall @ 1) improve.
          give_return: bool, if True, return computed metrics.
          epoch:       int, current epoch, required for logger.
      Returns:
          (optional) Computed metrics. Are normally written directly to LOG and printed.
      """
      start = time.time()
      image_paths = np.array(dataloader.dataset.image_list)
  
      with torch.no_grad():
          # Compute Metrics
          (
              F1,
              NMI,
              recall_at_ks,
              feature_matrix_all,
          ) = aux.eval_metrics_one_dataset(
              model,
              dataloader,
              device=opt.device,
              k_vals=opt.k_vals,
              opt=opt,
          )
          # Make printable summary string.
  
          result_str = ", ".join(
              "@{0}: {1:.4f}".format(k, rec)
              for k, rec in zip(opt.k_vals, recall_at_ks)
          )
          result_str = "Epoch (Test) {0}: NMI [{1:.4f}] | F1 [{2:.4f}] | Recall [{3}]".format(
              epoch, NMI, F1, result_str
          )
  
          if LOG is not None:
              if save:
                  if not len(
                      LOG.progress_saver["val"]["Recall @ 1"]
                  ) or recall_at_ks[0] > np.max(
                      LOG.progress_saver["val"]["Recall @ 1"]
                  ):
                      # Save Checkpoint
                      print(
                          "Set checkpoint at {}.".format(
                              LOG.prop.save_path
                              + "/checkpoint_{}.pth.tar".format(
                                  opt.iter
                              )
                          )
                      )
                      aux.set_checkpoint(
                          model,
                          opt,
                          LOG.progress_saver,
                          LOG.prop.save_path
                          + "/checkpoint_{}.pth.tar".format(opt.iter),
                      )
                      # aux.recover_closest_one_dataset(feature_matrix_all, image_paths, LOG.prop.save_path+'/sample_recoveries.png')
              # Update logs.
              LOG.log(
                  "val",
                  LOG.metrics_to_log["val"],
                  [epoch, np.round(time.time() - start), NMI, F1]
                  + recall_at_ks,
              )
  
      print(result_str)
      if give_return:
          return recall_at_ks, NMI, F1
      else:
          None
  
imports: import warnings
  import csv
  import pickle
  import time
  import auxiliaries
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  from scipy.spatial import distance
  from sklearn.preprocessing import normalize
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate_one_dataset(LOG, dataloader, model, opt, save, give_return, epoch) and returns: recall_at_ks,  NMI,  F1