class_docstr: Blurring an image by a Gaussian function with a certain probability.
  
  Args:
      prob (float, optional): A probability of blurring an image. Default: 0.1.
class_docstr_tok: ["Blurring","an","image","by","a","Gaussian","function","with","a","certain","probability",".","Args",":","prob","(","float",",","optional",")",":","A","probability","of","blurring","an","image",".","Default",":","0.1","."]
code: def __call__(self, data):
      if self.prob <= 0:
          n = 0
      elif self.prob >= 1:
          n = 1
      else:
          n = int(1.0 / self.prob)
      if n > 0:
          if np.random.randint(0, n) == 0:
              radius = np.random.randint(3, 10)
              if radius % 2 != 1:
                  radius = radius + 1
              if radius > 9:
                  radius = 9
              data["img"] = cv2.GaussianBlur(
                  data["img"], (radius, radius), 0, 0
              )
              for key in data.get("gt_fields", []):
                  if key == "trimap":
                      continue
                  data[key] = cv2.GaussianBlur(
                      data[key], (radius, radius), 0, 0
                  )
      return data
  
imports: import os
  import random
  import string
  import cv2
  import numpy
  from paddleseg.transforms import functional
  from paddleseg.cvlibs import manager
  from paddleseg.utils import seg_env
  from PIL import Image
label_desc: Use image node_id 2 for calling the RandomBlur obj's underlying member method code with example usage: obj.__call__(data) and returns: data