code: def scatter_time_vs_s(time, norm, point_labels, title):
      plt.figure()
      size = 100
      for i, l in enumerate(sorted(norm.keys())):
          if l != "fbpca":
              plt.scatter(
                  time[l], norm[l], label=l, marker="o", c="b", s=size
              )
              for label, x, y in zip(
                  point_labels, list(time[l]), list(norm[l])
              ):
                  plt.annotate(
                      label,
                      xy=(x, y),
                      xytext=(0, -80),
                      textcoords="offset points",
                      ha="right",
                      arrowprops=dict(
                          arrowstyle="->", connectionstyle="arc3"
                      ),
                      va="bottom",
                      size=11,
                      rotation=90,
                  )
          else:
              plt.scatter(
                  time[l], norm[l], label=l, marker="^", c="red", s=size
              )
              for label, x, y in zip(
                  point_labels, list(time[l]), list(norm[l])
              ):
                  plt.annotate(
                      label,
                      xy=(x, y),
                      xytext=(0, 30),
                      textcoords="offset points",
                      ha="right",
                      arrowprops=dict(
                          arrowstyle="->", connectionstyle="arc3"
                      ),
                      va="bottom",
                      size=11,
                      rotation=90,
                  )
  
      plt.legend(loc="best")
      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 5 for calling a global function with example usage: scatter_time_vs_s(time, norm, point_labels, title) without return types