code: def plot_with_matplotlib(x_vals, y_vals, labels):
      import matplotlib.pyplot as plt
      import random
  
      random.seed(0)
  
      plt.figure(figsize=(12, 12))
      plt.scatter(x_vals, y_vals)
  
      #
      # Label randomly subsampled 25 data points
      #
      indices = list(range(len(labels)))
      selected_indices = random.sample(indices, 25)
      for i in selected_indices:
          plt.annotate(labels[i], (x_vals[i], y_vals[i]))
  
imports: import logging
  import gensim.downloader
  from gensim.test.utils import datapath
  from gensim import utils
  import gensim.models
  import tempfile
  import os
  import io
  import os
  import gensim.models.word2vec
  import gensim.downloader
  import smart_open
  import time
  import numpy
  import pandas
  from sklearn.decomposition import IncrementalPCA
  from sklearn.manifold import TSNE
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: plot_with_matplotlib(x_vals, y_vals, labels) without return types