code: def gaussian_blur(
      img: np.ndarray, ksize: int, sigma: float = 0
  ) -> np.ndarray:
      # When sigma=0, it is computed as `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`
      blur_fn = _maybe_process_in_chunks(
          cv2.GaussianBlur, ksize=(ksize, ksize), sigmaX=sigma
      )
      return blur_fn(img)
  
file_path: albumentations/albumentations/augmentations/blur/functional.py
imports: from itertools import product
  from math import ceil
  from typing import Sequence, Union
  import cv2
  import numpy
  from albumentations.augmentations.functional import convolve
  from albumentations.augmentations.geometric.functional import scale
  from albumentations.augmentations.utils import _maybe_process_in_chunks, clipped, preserve_shape
label_desc: Use image node_id 3 for calling a global function with example usage: gaussian_blur(img, ksize, sigma) and returns: blur_fn