code: def _augmentation_params(self):
      scaling_factor = np.random.uniform(
          1 - self.max_scale_change, 1 + self.max_scale_change
      )
      if self.horizontal_flip:
          horizontal_flip = bool(np.random.choice(2))
      else:
          horizontal_flip = False
      vertical_shift = np.random.uniform(
          -self.max_vertical_shift, self.max_vertical_shift
      )
  
      return {
          "scaling_factor": scaling_factor,
          "horizontal_flip": horizontal_flip,
          "vertical_shift": vertical_shift,
      }
  
imports: import enum
  from copy import deepcopy
  import numpy
  from skimage import img_as_ubyte
  from skimage.transform import rescale, resize
  from .countless2d import zero_corrected_countless
label_desc: Use image node_id 6 for calling the SegmentationMask obj's underlying member method code with example usage: obj._augmentation_params() and returns: dict