code: def plot_time_vs_s(time, norm, point_labels, title):
      plt.figure()
      colors = ["g", "b", "y"]
      for i, l in enumerate(sorted(norm.keys())):
          if l != "fbpca":
              plt.plot(
                  time[l], norm[l], label=l, marker="o", c=colors.pop()
              )
          else:
              plt.plot(time[l], norm[l], label=l, marker="^", c="red")
  
          for label, x, y in zip(
              point_labels, list(time[l]), list(norm[l])
          ):
              plt.annotate(
                  label,
                  xy=(x, y),
                  xytext=(0, -20),
                  textcoords="offset points",
                  ha="right",
                  va="bottom",
              )
      plt.legend(loc="upper right")
      plt.suptitle(title)
      plt.ylabel("norm discrepancy")
      plt.xlabel("running time [s]")
  
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 4 for calling a global function with example usage: plot_time_vs_s(time, norm, point_labels, title) without return types