code: def PCA_components(x):
       
      # 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()]
docs: 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