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 _augment(self, img, v):
      old_dtype = img.dtype
      v = v * self.eigval
      v = v.reshape((3, 1))
      inc = np.dot(self.eigvec, v).reshape((3,))
      img = np.add(img, inc)
      if self.clip or old_dtype == np.uint8:
          img = np.clip(img, 0, 255)
      return img.astype(old_dtype)
  
imports: import numpy
  import cv2
  from ...utils.develop import log_deprecated
  from .base import PhotometricAugmentor
label_desc: Use image node_id 3 for calling the Lighting obj's underlying member method code with example usage: obj._augment(img, v) and returns: img