class_docstr: Apply CLAHE to all channels of images in their original colorspaces.
  
  CLAHE (Contrast Limited Adaptive Histogram Equalization) performs
  histogram equilization within image patches, i.e. over local
  neighbourhoods.
  
  In contrast to ``imgaug.augmenters.contrast.CLAHE``, this augmenter
  operates directly on all channels of the input images. It does not
  perform any colorspace transformations and does not focus on specific
  channels (e.g. ``L`` in ``Lab`` colorspace).
  
  **Supported dtypes**:
  
      * ``uint8``: yes; fully tested
      * ``uint16``: yes; tested
      * ``uint32``: no (1)
      * ``uint64``: no (2)
      * ``int8``: no (2)
      * ``int16``: no (2)
      * ``int32``: no (2)
      * ``int64``: no (2)
      * ``float16``: no (2)
      * ``float32``: no (2)
      * ``float64``: no (2)
      * ``float128``: no (1)
      * ``bool``: no (1)
  
      - (1) rejected by cv2
      - (2) results in error in cv2: ``cv2.error:
            OpenCV(3.4.2) (...)/clahe.cpp:351: error: (-215:Assertion
            failed) src.type() == (((0) & ((1 << 3) - 1)) + (((1)-1) << 3))
            || _src.type() == (((2) & ((1 << 3) - 1)) + (((1)-1) << 3)) in
            function 'apply'``
  
  Parameters
  ----------
  clip_limit : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      See ``imgaug.augmenters.contrast.CLAHE``.
  
  tile_grid_size_px : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional
      See ``imgaug.augmenters.contrast.CLAHE``.
  
  tile_grid_size_px_min : int, optional
      See ``imgaug.augmenters.contrast.CLAHE``.
  
  per_channel : bool or float, optional
      Whether to use the same value for all channels (``False``) or to
      sample a new value for each channel (``True``). If this value is a
      float ``p``, then for ``p`` percent of all images `per_channel` will
      be treated as ``True``, otherwise as ``False``.
  
  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.AllChannelsCLAHE()
  
  Create an augmenter that applies CLAHE to all channels of input images.
  
  >>> aug = iaa.AllChannelsCLAHE(clip_limit=(1, 10))
  
  Same as in the previous example, but the `clip_limit` used by CLAHE is
  uniformly sampled per image from the interval ``[1, 10]``. Some images
  will therefore have stronger contrast than others (i.e. higher clip limit
  values).
  
  >>> aug = iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True)
  
  Same as in the previous example, but the `clip_limit` is sampled per
  image *and* channel, leading to different levels of contrast for each
  channel.
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docstr: See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`.
docstr_tok: ["See",":","func",":","`","~imgaug.augmenters.meta.Augmenter.get_parameters","`","."]
code: def get_parameters(self):
      """See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
      return [
          self.clip_limit,
          self.tile_grid_size_px,
          self.tile_grid_size_px_min,
          self.per_channel,
      ]
  
imports: from __future__ import print_function, division, absolute_import
  import numpy
  import six.moves
  import skimage.exposure
  import cv2
  import imgaug
  from imgaug.imgaug import _normalize_cv2_input_arr_
  from .None import meta
  from .None import color
  from ..None import parameters
  from ..None import dtypes
  from ..augmentables.batches import _BatchInAugmentation
label_desc: Use image node_id 3 for calling the AllChannelsCLAHE obj's underlying member method code with example usage: obj.get_parameters() and returns: list