old_contents: import numpy
  
  
  def pca_lighting(img, sigma, eigen_value=None, eigen_vector=None):
      """Alter the intensities of input image using PCA.
  
      This is used in training of AlexNet [Krizhevsky]_.
  
      .. [Krizhevsky] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. \
      ImageNet Classification with Deep Convolutional Neural Networks. \
      NIPS 2012.
  
      Args:
          image (numpy.ndarray): An image array to be augmented. This is in
              CHW format.
          sigma (float): Standard deviation of the Gaussian. In AlexNet
              [Krizhevsky]_, 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 = numpy.array((0.2175, 0.0188, 0.0045))
      if eigen_vector is None:
          eigen_vector = numpy.array((
              (0.4009, -0.814,  0.4203),
              (0.7192, -0.0045, -0.6948),
              (-0.5675, -0.5808, -0.5836)))
  
      alpha = numpy.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
  
  
  def pca_lighting(img, sigma, eigen_value=None, eigen_vector=None):
      """Alter the intensities of input image using PCA.
  
      This 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:
          image (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 = numpy.array((0.2175, 0.0188, 0.0045))
      if eigen_vector is None:
          eigen_vector = numpy.array((
              (0.4009, -0.814,  0.4203),
              (0.7192, -0.0045, -0.6948),
              (-0.5675, -0.5808, -0.5836)))
  
      alpha = numpy.random.normal(0, sigma, size=3)
  
      img = img.copy()
      img += eigen_vector.dot(eigen_value * alpha).reshape(-1, 1, 1)
  
      return img
  
subject: fix citation style