code: def LDA_discriminants(x, labels):
      
      # validate inputs
      try:    
          x = np.array(x)
      except:
          raise ValueError('Impossible to convert x to a numpy array.')
      # make the LDA
      eigen_values, eigen_vectors = LDA_base(x, labels)
      return eigen_values[(-eigen_values).argsort()]
docs: Linear Discriminant Analysis helper for determination how many columns of
      data should be reduced.
  
      **Args:**
  
      * `x` : input matrix (2d array), every row represents new sample
  
      * `labels` : list of labels (iterable), every item should be label for \
          sample with corresponding index
  
      **Returns:**
      
      * `discriminants` : array of eigenvalues sorted in descending order