class_docstr: Add random Gaussian noise N(0, sigma^2) of the same shape to img.
class_docstr_tok: ["Add","random","Gaussian","noise","N","(","0",",","sigma^2",")","of","the","same","shape","to","img","."]
docstr: Args:
      sigma (float): stddev of the Gaussian distribution.
      clip (bool): clip the result to [0,255] in the end.
docstr_tok: ["Args",":","sigma","(","float",")",":","stddev","of","the","Gaussian","distribution",".","clip","(","bool",")",":","clip","the","result","to","[","0,255","]","in","the","end","."]
code: def __init__(self, sigma=1, clip=True):
      """
      Args:
          sigma (float): stddev of the Gaussian distribution.
          clip (bool): clip the result to [0,255] in the end.
      """
      super(GaussianNoise, self).__init__()
      self._init(locals())
  
file_path: tensorpack/tensorpack/dataflow/imgaug/noise.py
imports: import numpy
  import cv2
  from .base import PhotometricAugmentor
label_desc: Use image node_id 1 to create a new GaussianNoise object from inherited base classes: PhotometricAugmentor with example: obj = GaussianNoise(sigma, clip)