code: def pca_svd(x):
      
      w, s, _ = np.linalg.svd(x, full_matrices=False)
      return w, s ** 2
docs: Calculate PCA using SVD.
      
      Parameters
      ----------
      x : ndarray, shape (channels, samples)
          Two-dimensional input data.
          
      Returns
      -------
      w : ndarray, shape (channels, channels)
          Eigenvectors (principal components) (in columns).
      s : ndarray, shape (channels,)
          Eigenvalues.