code: def __call__(self, data):
      if np.random.rand() > self.prob:
          return data
      mean = np.random.uniform(0, 0.04)
      var = np.random.uniform(0, 0.001)
      noise = (
          np.random.normal(mean, var**0.5, data["img"].shape) * 255
      )
      data["img"] = data["img"] + noise
      data["img"] = np.clip(data["img"], 0, 255)
  
      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 RandomNoise obj's underlying member method code with example usage: obj.__call__(data) and returns: data, data