code: def evaluate(dataset, LOG, **kwargs):
      """
      Given a dataset name, applies the correct evaluation function.
  
      Args:
          dataset: str, name of dataset.
          LOG:     aux.LOGGER instance, main logging class.
          **kwargs: Input Argument Dict, depends on dataset.
      Returns:
          (optional) Computed metrics. Are normally written directly to LOG and printed.
      """
      if dataset in ["Inaturalist", "semi_fungi"]:
          ret = evaluate_one_dataset(LOG, **kwargs)
      elif dataset in ["vehicle_id"]:
          ret = evaluate_multiple_datasets(LOG, **kwargs)
      else:
          raise Exception("No implementation for dataset {} available!")
  
      return ret
  
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 1 for calling a global function with example usage: evaluate(dataset, LOG) and returns: ret