old_contents: import numpy as np
  
  
  def pca_lighting(img, sigma, eigen_value=None, eigen_vector=None):
      """AlexNet style color augmentation
  
      This method adds a noise vector drawn from a Gaussian. The direction of
      the Gaussian is same as that of the principal components of the dataset.
  
      This method is used in training of AlexNet [1].
  
      .. [1] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. \
      ImageNet Classification with Deep Convolutional Neural Networks. \
      NIPS 2012.
  
      Args:
          img (~numpy.ndarray): An image array to be augmented. This is in
              CHW format.
          sigma (float): Standard deviation of the Gaussian. In the original
              paper, this value is 10% of the range of intensity
              (25.5 if the range is [0, 255]).
          eigen_value: (~numpy.ndarray): An array of eigen values. The shape
              have to be (3,). If it is not specified, the values computed from
              ImageNet are used.
          eigen_vector: (~numpy.ndarray): An array of eigen vectors. The shape
              have to be (3, 3). If it is not specified, the vectors computed
              from ImageNet are used.
  
      Returns:
          An image in CHW format.
      """
  
      if sigma <= 0:
          return img
  
      # these values are copied from facebook/fb.resnet.torch
      if eigen_value is None:
          eigen_value = np.array((0.2175, 0.0188, 0.0045))
      if eigen_vector is None:
          eigen_vector = np.array((
              (0.4009, -0.814,  0.4203),
              (0.7192, -0.0045, -0.6948),
              (-0.5675, -0.5808, -0.5836)))
  
      alpha = np.random.normal(0, sigma, size=3)
  
      img = img.copy()
      img += eigen_vector.dot(eigen_value * alpha).reshape(-1, 1, 1)
  
      return img
  
new_contents: import numpy as np
  
  
  def pca_lighting(img, sigma, eigen_value=None, eigen_vector=None):
      """AlexNet style color augmentation
  
      This method adds a noise vector drawn from a Gaussian. The direction of
      the Gaussian is same as that of the principal components of the dataset.
  
      This method is used in training of AlexNet [1].
  
      .. [1] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. \
      ImageNet Classification with Deep Convolutional Neural Networks. \
      NIPS 2012.
  
      Args:
          img (~numpy.ndarray): An image array to be augmented. This is in
              CHW format.
          sigma (float): Standard deviation of the Gaussian. In the original
              paper, this value is 10% of the range of intensity
              (25.5 if the range is [0, 255]).
          eigen_value (~numpy.ndarray): An array of eigen values. The shape
              have to be (3,). If it is not specified, the values computed from
              ImageNet are used.
          eigen_vector (~numpy.ndarray): An array of eigen vectors. The shape
              have to be (3, 3). If it is not specified, the vectors computed
              from ImageNet are used.
  
      Returns:
          An image in CHW format.
      """
  
      if sigma <= 0:
          return img
  
      # these values are copied from facebook/fb.resnet.torch
      if eigen_value is None:
          eigen_value = np.array((0.2175, 0.0188, 0.0045))
      if eigen_vector is None:
          eigen_vector = np.array((
              (0.4009, -0.814,  0.4203),
              (0.7192, -0.0045, -0.6948),
              (-0.5675, -0.5808, -0.5836)))
  
      alpha = np.random.normal(0, sigma, size=3)
  
      img = img.copy()
      img += eigen_vector.dot(eigen_value * alpha).reshape(-1, 1, 1)
  
      return img
  
subject: fix pca_lightning doc to remove unnecessary dash