class_docstr: Blur the input image using a Gaussian filter with a random kernel size.
  
  Args:
      blur_limit (int, (int, int)): 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).
      sigma_limit (float, (float, float)): Gaussian kernel standard deviation. Must be in range [0, inf).
          If set single value `sigma_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.
      p (float): probability of applying the transform. Default: 0.5.
  
  Targets:
      image
  
  Image types:
      uint8, float32
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code: def apply(
      self, img: np.ndarray, ksize: int = 3, sigma: float = 0, **params
  ) -> np.ndarray:
      return F.gaussian_blur(img, ksize, sigma=sigma)
  
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 2 for calling the GaussianBlur obj's underlying member method code with example usage: obj.apply(img, ksize, sigma) and returns: F