class_docstr: Lighting noise, as in the paper
  `ImageNet Classification with Deep Convolutional Neural Networks
  <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_.
  The implementation follows `fb.resnet.torch
  <https://github.com/facebook/fb.resnet.torch/blob/master/datasets/transforms.lua#L184>`__.
class_docstr_tok: ["Lighting","noise",",","as","in","the","paper","`","ImageNet","Classification","with","Deep","Convolutional","Neural","Networks","<","https",":","\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf",">","`","_",".","The","implementation","follows","`","fb.resnet.torch","<","https",":","\/\/github.com\/facebook\/fb.resnet.torch\/blob\/master\/datasets\/transforms.lua","#","L184",">","`","__","."]
code: def _get_augment_params(self, img):
      assert img.shape[2] == 3
      return (self.rng.randn(3) * self.std).astype("float32")
  
imports: import numpy
  import cv2
  from ...utils.develop import log_deprecated
  from .base import PhotometricAugmentor
label_desc: Use image node_id 2 for calling the Lighting obj's underlying member method code with example usage: obj._get_augment_params(img) and returns: unknown