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"]
code: def wrap_data(data):
      if isinstance(data, np.ndarray):
          data = DataContainer((data,), "I")
      elif isinstance(data, dict):
          data = DataContainer.from_dict(data)
      elif not isinstance(data, DataContainer):
          raise TypeError("Unknown data type!")
      return data
  
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 6 for calling the BaseTransform obj's underlying member method code with example usage: obj.wrap_data(data) and returns: data