class_docstr: Lighting noise(AlexNet - style PCA - based noise).
  This trick was originally used in `AlexNet paper
  <https://papers.nips.cc/paper/4824-imagenet-classification
  -with-deep-convolutional-neural-networks.pdf>`_
  
  The eigen values and eigen vectors, are taken from caffe2 `ImageInputOp.h
  <https://github.com/pytorch/pytorch/blob/master/caffe2/image/
  image_input_op.h#L265>`_.
code: def __init__(
      self,
      alphastd=_DEFAULT_COLOR_LIGHTING_STD,
      eigval=_IMAGENET_EIGEN_VAL,
      eigvec=_IMAGENET_EIGEN_VEC,
  ):
      self.alphastd = alphastd
      self.eigval = torch.tensor(eigval)
      # Divide by 255 as the Lighting operation is expected to be applied
      # on `img` pixels ranging between [0.0, 1.0]
      self.eigvec = torch.tensor(eigvec) / 255.0
  
imports: import torch
  from .None import register_transform
  from .classy_transform import ClassyTransform
label_desc: Use image node_id 1 to create a new LightingTransform object from inherited base classes: ClassyTransform with example: obj = LightingTransform(alphastd, eigval, eigvec)