code: def _compute_gaussian_blur_ksize(sigma):
      if sigma < 3.0:
          ksize = 3.3 * sigma  # 99% of weight
      elif sigma < 5.0:
          ksize = 2.9 * sigma  # 97% of weight
      else:
          ksize = 2.6 * sigma  # 95% of weight
  
      # we use 5x5 here as the minimum size as that simplifies
      # comparisons with gaussian_filter() in the tests
      # TODO reduce this to 3x3
      ksize = int(max(ksize, 5))
      if ksize % 2 == 0:
          ksize += 1
      return ksize
  
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 4 for calling a global function with example usage: _compute_gaussian_blur_ksize(sigma) and returns: ksize