class_docstr: Convert the style of images to a more cartoonish one.
  
  This augmenter was primarily designed for images with a size of ``200``
  to ``800`` pixels. Smaller or larger images may cause issues.
  
  Note that the quality of the results can currently not compete with
  learned style transfer, let alone human-made images. A lack of detected
  edges or also too many detected edges are probably the most significant
  drawbacks.
  
  Added in 0.4.0.
  
  **Supported dtypes**:
  
  See :func:`~imgaug.augmenters.artistic.stylize_cartoon`.
  
  Parameters
  ----------
  blur_ksize : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      Median filter kernel size.
      See :func:`~imgaug.augmenters.artistic.stylize_cartoon` for details.
  
          * If ``number``: That value will be used for all images.
          * If ``tuple (a, b) of number``: A random value will be uniformly
            sampled per image from the interval ``[a, b)``.
          * If ``list``: A random value will be picked per image from the
            ``list``.
          * If ``StochasticParameter``: The parameter will be queried once
            per batch for ``(N,)`` values, where ``N`` is the number of
            images.
  
  segmentation_size : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      Mean-Shift segmentation size multiplier.
      See :func:`~imgaug.augmenters.artistic.stylize_cartoon` for details.
  
          * If ``number``: That value will be used for all images.
          * If ``tuple (a, b) of number``: A random value will be uniformly
            sampled per image from the interval ``[a, b)``.
          * If ``list``: A random value will be picked per image from the
            ``list``.
          * If ``StochasticParameter``: The parameter will be queried once
            per batch for ``(N,)`` values, where ``N`` is the number of
            images.
  
  saturation : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      Saturation multiplier.
      See :func:`~imgaug.augmenters.artistic.stylize_cartoon` for details.
  
          * If ``number``: That value will be used for all images.
          * If ``tuple (a, b) of number``: A random value will be uniformly
            sampled per image from the interval ``[a, b)``.
          * If ``list``: A random value will be picked per image from the
            ``list``.
          * If ``StochasticParameter``: The parameter will be queried once
            per batch for ``(N,)`` values, where ``N`` is the number of
            images.
  
  edge_prevalence : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      Multiplier for the prevalence of edges.
      See :func:`~imgaug.augmenters.artistic.stylize_cartoon` for details.
  
          * If ``number``: That value will be used for all images.
          * If ``tuple (a, b) of number``: A random value will be uniformly
            sampled per image from the interval ``[a, b)``.
          * If ``list``: A random value will be picked per image from the
            ``list``.
          * If ``StochasticParameter``: The parameter will be queried once
            per batch for ``(N,)`` values, where ``N`` is the number of
            images.
  
  from_colorspace : str, optional
      The source colorspace. Use one of ``imgaug.augmenters.color.CSPACE_*``.
      Defaults to ``RGB``.
  
  seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
      See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
  
  name : None or str, optional
      See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
  
  random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
      Old name for parameter `seed`.
      Its usage will not yet cause a deprecation warning,
      but it is still recommended to use `seed` now.
      Outdated since 0.4.0.
  
  deterministic : bool, optional
      Deprecated since 0.4.0.
      See method ``to_deterministic()`` for an alternative and for
      details about what the "deterministic mode" actually does.
  
  Examples
  --------
  >>> import imgaug.augmenters as iaa
  >>> aug = iaa.Cartoon()
  
  Create an example image, then apply a cartoon filter to it.
  
  >>> aug = iaa.Cartoon(blur_ksize=3, segmentation_size=1.0,
  >>>                   saturation=2.0, edge_prevalence=1.0)
  
  Create a non-stochastic cartoon augmenter that produces decent-looking
  images.
code: def _draw_samples(self, batch, random_state):
      nb_rows = batch.nb_rows
      return (
          self.blur_ksize.draw_samples(
              (nb_rows,), random_state=random_state
          ),
          self.segmentation_size.draw_samples(
              (nb_rows,), random_state=random_state
          ),
          self.saturation.draw_samples(
              (nb_rows,), random_state=random_state
          ),
          self.edge_prevalence.draw_samples(
              (nb_rows,), random_state=random_state
          ),
      )
  
imports: from __future__ import print_function, division, absolute_import
  import numpy
  import cv2
  from imgaug.imgaug import _normalize_cv2_input_arr_
  from .None import meta
  from .None import color
  from ..None import dtypes
  from ..None import parameters
label_desc: Use image node_id 3 for calling the Cartoon obj's underlying member method code with example usage: obj._draw_samples(batch, random_state) and returns: self,  self,  self,  self