class_docstr: Implementation of random Gaussian blur.
  
  Utilizes the built-in ImageFilter method from PIL to apply a Gaussian
  blur to the input image with a certain probability. The blur is further
  randomized by sampling uniformly the values of the standard deviation of
  the Gaussian kernel.
  
  Attributes:
      kernel_size:
          Will be deprecated in favor of `sigmas` argument. If set, the old behavior applies and `sigmas` is ignored.
          Used to calculate sigma of gaussian blur with kernel_size * input_size.
      prob:
          Probability with which the blur is applied.
      scale:
          Will be deprecated in favor of `sigmas` argument. If set, the old behavior applies and `sigmas` is ignored.
          Used to scale the `kernel_size` of a factor of `kernel_scale`
      sigmas:
          Tuple of min and max value from which the std of the gaussian kernel is sampled.
          Is ignored if `kernel_size` is set.
code: def __init__(
      self,
      kernel_size: Union[float, None] = None,
      prob: float = 0.5,
      scale: Union[float, None] = None,
      sigmas: Tuple[float, float] = (0.2, 2),
  ):
      if scale != None or kernel_size != None:
          warn(
              "The 'kernel_size' and 'scale' arguments of the GaussianBlur augmentation will be deprecated.  "
              "Please use the 'sigmas' parameter instead.",
              DeprecationWarning,
          )
      self.prob = prob
      self.sigmas = sigmas
  
file_path: lightly/lightly/transforms/gaussian_blur.py
imports: from typing import Tuple, Union
  from warnings import warn
  import numpy
  from PIL import ImageFilter
  from PIL.Image import Image
label_desc: Use image node_id 1 to create a new GaussianBlur object  with example: obj = GaussianBlur(kernel_size, prob, scale, sigmas)