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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 LDA_discriminants(x, labels):
 """ 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 """

    # 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()]