code: def plot_power_iter_vs_s(power_iter, s, title):
      plt.figure()
      for l in sorted(s.keys()):
          plt.plot(power_iter, s[l], label=l, marker="o")
      plt.legend(loc="lower right", prop={"size": 10})
      plt.suptitle(title)
      plt.ylabel("norm discrepancy")
      plt.xlabel("n_iter")
  
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 6 for calling a global function with example usage: plot_power_iter_vs_s(power_iter, s, title) without return types