class_docstr: Blur the input image using a Generalized Normal filter with a randomly selected parameters.
      This transform also adds multiplicative noise to generated kernel before convolution.
  
  Args:
      blur_limit: maximum Gaussian kernel size for blurring the input image.
          Must be zero or odd and in range [0, inf). If set to 0 it will be computed from sigma
          as `round(sigma * (3 if img.dtype == np.uint8 else 4) * 2 + 1) + 1`.
          If set single value `blur_limit` will be in range (0, blur_limit).
          Default: (3, 7).
      sigmaX_limit: Gaussian kernel standard deviation. Must be in range [0, inf).
          If set single value `sigmaX_limit` will be in range (0, sigma_limit).
          If set to 0 sigma will be computed as `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. Default: 0.
      sigmaY_limit: Same as `sigmaY_limit` for another dimension.
      rotate_limit: Range from which a random angle used to rotate Gaussian kernel is picked.
          If limit is a single int an angle is picked from (-rotate_limit, rotate_limit). Default: (-90, 90).
      beta_limit: Distribution shape parameter, 1 is the normal distribution. Values below 1.0 make distribution
          tails heavier than normal, values above 1.0 make it lighter than normal. Default: (0.5, 8.0).
      noise_limit: Multiplicative factor that control strength of kernel noise. Must be positive and preferably
          centered around 1.0. If set single value `noise_limit` will be in range (0, noise_limit).
          Default: (0.75, 1.25).
      p (float): probability of applying the transform. Default: 0.5.
  
  Reference:
      https://arxiv.org/abs/2107.10833
  
  Targets:
      image
  Image types:
      uint8, float32
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code: def get_transform_init_args_names(
      self,
  ) -> Tuple[str, str, str, str, str, str]:
      return (
          "blur_limit",
          "sigmaX_limit",
          "sigmaY_limit",
          "rotate_limit",
          "beta_limit",
          "noise_limit",
      )
  
imports: import random
  import warnings
  from typing import Any, Dict, List, Sequence, Tuple
  import cv2
  import numpy
  from albumentations import random_utils
  from albumentations.augmentations import functional
  from albumentations.augmentations.blur import functional
  from albumentations.core.transforms_interface import ImageOnlyTransform, ScaleFloatType, ScaleIntType, to_tuple
label_desc: Use image node_id 5 for calling the AdvancedBlur obj's underlying member method code with example usage: obj.get_transform_init_args_names() and returns: str,  str,  str,  str,  str,  str