class_docstr: Apply Histogram Eq. to all channels of images in their original colorspaces.
  
  In contrast to ``imgaug.augmenters.contrast.HistogramEqualization``, 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``: no (1)
      * ``uint32``: no (2)
      * ``uint64``: no (1)
      * ``int8``: no (1)
      * ``int16``: no (1)
      * ``int32``: no (1)
      * ``int64``: no (1)
      * ``float16``: no (2)
      * ``float32``: no (1)
      * ``float64``: no (1)
      * ``float128``: no (2)
      * ``bool``: no (1)
  
      - (1) causes cv2 error: ``cv2.error:
            OpenCV(3.4.5) (...)/histogram.cpp:3345: error: (-215:Assertion
            failed) src.type() == CV_8UC1 in function 'equalizeHist'``
      - (2) rejected by cv2
  
  Parameters
  ----------
  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      Outdated since 0.4.0.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.AllChannelsHistogramEqualization()
  
  Create an augmenter that applies histogram equalization to all channels
  of input images in the original colorspaces.
  
  >>> aug = iaa.Alpha((0.0, 1.0), iaa.AllChannelsHistogramEqualization())
  
  Same as in the previous example, but alpha-blends the contrast-enhanced
  augmented images with the original input images using random blend
  strengths. This leads to random strengths of the contrast adjustment.
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code: def __init__(
      self,
      seed=None,
      name=None,
      random_state="deprecated",
      deterministic="deprecated",
  ):
      super(AllChannelsHistogramEqualization, self).__init__(
          seed=seed,
          name=name,
          random_state=random_state,
          deterministic=deterministic,
      )
  
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 1 to create a new AllChannelsHistogramEqualization object from inherited base classes: meta with example: obj = AllChannelsHistogramEqualization(seed, name, random_state, deterministic)