code: def visualize_clusters(
      model_name,
      X,
      predicted_labels,
      show_figure=True,
      save_figure=False,
  ):  # pragma: no cover
      """Utility function for visualizing the results in examples.
      Internal use only.
  
      Parameters
      ----------
      model_name : str
          The name of the clustering method.
  
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      predicted_labels : numpy array of shape (n_samples, n_features)
          The predicted labels of the input samples.
  
      show_figure : bool, optional (default=True)
          If set to True, show the figure.
  
      save_figure : bool, optional (default=False)
          If set to True, save the figure to the local.
  
      """
      colors = np.array(
          list(
              islice(
                  cycle(
                      [
                          "#377eb8",
                          "#ff7f00",
                          "#4daf4a",
                          "#f781bf",
                          "#a65628",
                          "#984ea3",
                          "#999999",
                          "#e41a1c",
                          "#dede00",
                      ]
                  ),
                  int(max(predicted_labels) + 1),
              )
          )
      )
  
      plt.scatter(
          X[:, 0], X[:, 1], s=10, color=colors[predicted_labels]
      )
      plt.title(model_name)
      plt.xticks(())
      plt.yticks(())
  
      if save_figure:
          plt.savefig(
              "{clf_name}.png".format(clf_name=model_name), dpi=300
          )
  
      if show_figure:
          plt.show()
  
label_desc: Use image node_id 1 for calling a global function with example usage: visualize_clusters(model_name, X, predicted_labels, show_figure, save_figure) without return types