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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

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])