class_docstr: Transformation abstract class.
  
  Parameters
  ----------
  p : float or None
      Probability of executing this transform
  data_indices : tuple or None
      Indices where the transforms need to be applied
class_docstr_tok: ["Transformation","abstract","class",".","Parameters","--","--","--","--","--","p",":","float","or","None","Probability","of","executing","this","transform","data_indices",":","tuple","or","None","Indices","where","the","transforms","need","to","be","applied"]
docstr: Abstract method, which determines the transform's behaviour when it is applied to labels (e.g. label smoothing)
  
  Parameters
  ----------
  labels : numpy.ndarray
      Array of labels.
  
  Returns
  -------
  out : numpy.ndarray
      Result
code: def _apply_labels(self, labels, settings: np.ndarray):
      """
      Abstract method, which determines the transform's behaviour when it is applied to labels (e.g. label smoothing)
  
      Parameters
      ----------
      labels : numpy.ndarray
          Array of labels.
  
      Returns
      -------
      out : numpy.ndarray
          Result
  
      """
  
imports: import copy
  import random
  from abc import ABCMeta, abstractmethod
  import cv2
  import numpy
  from solt.utils import Serializable
  from solt.constants import ALLOWED_INTERPOLATIONS, ALLOWED_PADDINGS
  from ._data import DataContainer, Keypoints
  from solt.utils import img_shape_checker, validate_parameter
label_desc: Use image node_id 10 for calling the BaseTransform obj's underlying member method code with example usage: obj._apply_labels(labels, settings) without return types