code: def norm_diff(A, norm=2, msg=True, random_state=None):
      """
      Compute the norm diff with the original matrix, when randomized
      SVD is called with *params.
  
      norm: 2 => spectral; 'fro' => Frobenius
      """
  
      if msg:
          print("... computing %s norm ..." % norm)
      if norm == 2:
          # s = sp.linalg.norm(A, ord=2)  # slow
          v0 = _init_arpack_v0(min(A.shape), random_state)
          value = sp.sparse.linalg.svds(
              A, k=1, return_singular_vectors=False, v0=v0
          )
      else:
          if sp.sparse.issparse(A):
              value = sp.sparse.linalg.norm(A, ord=norm)
          else:
              value = sp.linalg.norm(A, ord=norm)
      return value
  
imports: import gc
  import os.path
  import pickle
  from collections import defaultdict
  from time import time
  import matplotlib.pyplot
  import numpy
  import scipy
  from sklearn.datasets import fetch_20newsgroups_vectorized, fetch_lfw_people, fetch_olivetti_faces, fetch_openml, fetch_rcv1, make_low_rank_matrix, make_sparse_uncorrelated
  from sklearn.utils import gen_batches
  from sklearn.utils._arpack import _init_arpack_v0
  from sklearn.utils.extmath import randomized_svd
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 8 for calling a global function with example usage: norm_diff(A, norm, msg, random_state) and returns: value