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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 PCA_components(x):
 """ Principal Component Analysis helper to check out eigenvalues of components. **Args:** * `x` : input matrix (2d array), every row represents new sample **Returns:** * `components`: sorted array of principal components eigenvalues """
 
    # validate inputs
    try:    
        x = np.array(x)
    except:
        raise ValueError('Impossible to convert x to a numpy array.')
    # eigen values and eigen vectors of data covariance matrix
    eigen_values, eigen_vectors = np.linalg.eig(np.cov(x.T))
    # sort eigen vectors according biggest eigen value
    eigen_order = eigen_vectors.T[(-eigen_values).argsort()]
    # form output - order the eigenvalues
    return eigen_values[(-eigen_values).argsort()]