code: def evaluate_multiple_datasets(
      LOG, dataloaders, model, opt, save=True, give_return=True, epoch=0
  ):
      """
      Compute evaluation metrics, update LOGGER and print results, specifically for Multi-test datasets s.a. PKU Vehicle ID.
  
      Args:
          LOG:         aux.LOGGER-instance. Main Logging Functionality.
          dataloaders: List of PyTorch Dataloaders, test-dataloaders to evaluate.
          model:       PyTorch Network, Network to evaluate.
          opt:         argparse.Namespace, contains all training-specific parameters.
          sa ve:        bool, if True, Checkpoints are saved when testing metrics (specifically Recall @ 1) improve.
          give_return: bool, i f 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()
  
      csv_data = [epoch]
  
      with torch.no_grad():
          for i, dataloader in enumerate(dataloaders):
              print(
                  "Working on Set {}/{}".format(i + 1, len(dataloaders))
              )
              image_paths = np.array(dataloader.dataset.image_list)
              # Compute Metrics for specific testset.
              (
                  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,
              )
              # Generate 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 = "SET {0}: Epoch (Test) {1}: NMI [{2:.4f}] | F1 {3:.4f}| Recall [{4}]".format(
                  i + 1, epoch, NMI, F1, result_str
              )
  
              if LOG is not None:
                  if save:
                      if not len(
                          LOG.progress_saver["val"][
                              "Set {} Recall @ 1".format(i)
                          ]
                      ) or recall_at_ks[0] > np.max(
                          LOG.progress_saver["val"][
                              "Set {} Recall @ 1".format(i)
                          ]
                      ):
                          # Save Checkpoint for specific test set.
                          aux.set_checkpoint(
                              model,
                              opt,
                              LOG.progress_saver,
                              LOG.prop.save_path
                              + "/checkpoint_set{}.pth.tar".format(
                                  i + 1
                              ),
                          )
                          aux.recover_closest_one_dataset(
                              feature_matrix_all,
                              image_paths,
                              LOG.prop.save_path
                              + "/sample_recoveries_set{}.png".format(
                                  i + 1
                              ),
                          )
  
                  csv_data += [NMI, F1] + recall_at_ks
              print(result_str)
  
      csv_data.insert(0, np.round(time.time() - start))
      # Update logs.
      LOG.log("val", LOG.metrics_to_log["val"], csv_data)
  
      # if give_return:
      return csv_data[2:]
  
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 4 for calling a global function with example usage: evaluate_multiple_datasets(LOG, dataloaders, model, opt, save, give_return, epoch) and returns: csv_data