code: def __mlcm_vector_handler__(
      mlcm, actual_vector, predict_vector, sample_weight, classes
  ):
      """
      Handle multilabel object conditions for vectors.
  
      :param mlcm: multilabel confusion matrix
      :type mlcm: pycm.MultiLabelCM object
      :param actual_vector: actual vector
      :type actual_vector: python list or numpy array of sets
      :param predict_vector: vector of predictions
      :type predict_vector: python list or numpy array of sets
      :param sample_weight: sample weights list
      :type sample_weight: python list or numpy array
      :param classes: ordered labels of classes
      :type classes: list
      :return: None
      """
      if not isinstance(
          actual_vector, (list, numpy.ndarray)
      ) or not isinstance(predict_vector, (list, numpy.ndarray)):
          raise pycmVectorError(VECTOR_TYPE_ERROR)
      if len(actual_vector) != len(predict_vector):
          raise pycmVectorError(VECTOR_SIZE_ERROR)
      if len(actual_vector) == 0 or len(predict_vector) == 0:
          raise pycmVectorError(VECTOR_EMPTY_ERROR)
      if not all(map(lambda x: isinstance(x, set), actual_vector)):
          raise pycmVectorError(NOT_ALL_SET_VECTOR_ERROR)
      if not all(map(lambda x: isinstance(x, set), predict_vector)):
          raise pycmVectorError(NOT_ALL_SET_VECTOR_ERROR)
      if classes is not None and len(set(classes)) != len(classes):
          raise pycmVectorError(VECTOR_UNIQUE_CLASS_ERROR)
      if isinstance(sample_weight, (list, numpy.ndarray)):
          mlcm.weights = sample_weight
  
file_path: pycm/pycm/pycm_multilabel_cm.py
imports: from __future__ import division
  from .pycm_error import pycmVectorError, pycmMultiLabelError
  from .pycm_param import *
  from .pycm_obj import ConfusionMatrix
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: __mlcm_vector_handler__(mlcm, actual_vector, predict_vector, sample_weight, classes) without return types