code: def randomized_pca(data, n_componets):
      """
      Randomized PCA based on the scikit-learn
      implementation.
  
      Parameters
      ----------
      data : 2d array-like
  
      n_components : int
          Number of PCA components
  
      Returns
      -------
      (eigenvectors, eigenvalues)
      """
      n_samples, n_features = data.shape
  
      U, S, V = randomized_svd(data, n_componets)
      eigenvalues = (S**2) / n_samples
      eigenvectors = V / S[:, np.newaxis] * np.sqrt(n_samples)
  
      return eigenvectors, eigenvalues
  
imports: import numpy
  from scipy import linalg
label_desc: Use image node_id 4 for calling a global function with example usage: randomized_pca(data, n_componets) and returns: eigenvectors,  eigenvalues