class_docstr: Augmenter to blur images using gaussian kernels.
  
  **Supported dtypes**:
  
  See ``~imgaug.augmenters.blur.blur_gaussian_(backend="auto")``.
  
  Parameters
  ----------
  sigma : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      Standard deviation of the gaussian kernel.
      Values in the range ``0.0`` (no blur) to ``3.0`` (strong blur) are
      common.
  
          * If a single ``float``, that value will always be used as the
            standard deviation.
          * If a tuple ``(a, b)``, then a random value from the interval
            ``[a, b]`` will be picked per image.
          * If a list, then a random value will be sampled per image from
            that list.
          * If a ``StochasticParameter``, then ``N`` samples will be drawn
            from that parameter per ``N`` input images.
  
  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      Outdated since 0.4.0.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.GaussianBlur(sigma=1.5)
  
  Blur all images using a gaussian kernel with a standard deviation of
  ``1.5``.
  
  >>> aug = iaa.GaussianBlur(sigma=(0.0, 3.0))
  
  Blur images using a gaussian kernel with a random standard deviation
  sampled uniformly (per image) from the interval ``[0.0, 3.0]``.
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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.sigma]
  
imports: from __future__ import print_function, division, absolute_import
  import numpy
  from scipy import ndimage
  import cv2
  import six.moves
  import imgaug
  from imgaug.imgaug import _normalize_cv2_input_arr_
  from .None import meta
  from .None import convolutional
  from ..None import parameters
  from ..None import dtypes
label_desc: Use image node_id 3 for calling the GaussianBlur obj's underlying member method code with example usage: obj.get_parameters() and returns: list