code: def normalize(X, norm):
    '''Applies the given norm to the input data set
  
  
    Parameters:
  
      X (numpy.ndarray): A 3D numpy ndarray in which the rows represent examples
        while the columns, features of the data set you want to normalize. Every
        depth corresponds to data for a particular class
  
      norm (tuple): A tuple containing two 1D numpy ndarrays corresponding to the
        normalization parameters extracted with :py:func:`estimated_norm` above.
  
  
     Returns:
  
       numpy.ndarray: A 3D numpy ndarray with the same dimensions as the input
         array ``X``, but with its values normalized according to the norm input.
  
    '''
  
    return numpy.array([(k - norm[0]) / norm[1] for k in X]f normalize(X, norm):
    '''Applies the given norm to the input data set
  
  
    Parameters:
  
      X (numpy.ndarray): A 3D numpy ndarray in which the rows represent examples
        while the columns, features of the data set you want to normalize. Every
        depth corresponds to data for a particular class
  
      norm (tuple): A tuple containing two 1D numpy ndarrays corresponding to the
        normalization parameters extracted with :py:func:`estimated_norm` above.
  
  
     Returns:
  
       numpy.ndarray: A 3D numpy ndarray with the same dimensions as the input
         array ``X``, but with its values normalized according to the norm input.
  
    '''
  
    return numpy.array([(k - norm[0]) / norm[1] for k in X])
docs: Applies the given norm to the input data set
  
  
    Parameters:
  
      X (numpy.ndarray): A 3D numpy ndarray in which the rows represent examples
        while the columns, features of the data set you want to normalize. Every
        depth corresponds to data for a particular class
  
      norm (tuple): A tuple containing two 1D numpy ndarrays corresponding to the
        normalization parameters extracted with :py:func:`estimated_norm` above.
  
  
     Returns:
  
       numpy.ndarray: A 3D numpy ndarray with the same dimensions as the input
         array ``X``, but with its values normalized according to the norm input.