code: def findEuclideanDistance(
      source_representation: Union[np.ndarray, list],
      test_representation: Union[np.ndarray, list],
  ) -> np.float64:
      if isinstance(source_representation, list):
          source_representation = np.array(source_representation)
  
      if isinstance(test_representation, list):
          test_representation = np.array(test_representation)
  
      euclidean_distance = source_representation - test_representation
      euclidean_distance = np.sum(
          np.multiply(euclidean_distance, euclidean_distance)
      )
      euclidean_distance = np.sqrt(euclidean_distance)
      return euclidean_distance
  
imports: from typing import Union
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: findEuclideanDistance(source_representation, test_representation) and returns: euclidean_distance