code: def data_visualize(
      X_train, y_train, show_figure=True, save_figure=False
  ):  # pragma: no cover
      """Utility function for visualizing the synthetic samples generated by
      generate_data_cluster function.
  
      Parameters
      ----------
      X_train : numpy array of shape (n_samples, n_features)
          The training samples.
  
      y_train : list or array of shape (n_samples,)
          The ground truth of training 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.
  
      """
  
      def _plot(
          X_inliers,
          X_outliers,
          inlier_color="blue",
          outlier_color="orange",
      ):
          """Internal method to add subplot of inliers and outliers.
  
          Parameters
          ----------
          X_inliers : numpy array of shape (n_samples, n_features)
              Outliers.
  
          X_outliers : numpy array of shape (n_samples, n_features)
              Inliers.
  
          sub_plot_title : str
              Subplot title.
  
          inlier_color : str, optional (default='blue')
              The color of inliers.
  
          outlier_color : str, optional (default='orange')
              The color of outliers.
  
          """
          plt.axis("equal")
          plt.scatter(
              X_inliers[:, 0],
              X_inliers[:, 1],
              label="inliers",
              color=inlier_color,
              s=40,
          )
          plt.scatter(
              X_outliers[:, 0],
              X_outliers[:, 1],
              label="outliers",
              color=outlier_color,
              s=50,
              marker="^",
          )
          plt.xticks([])
          plt.yticks([])
          plt.legend(loc="best", prop={"size": 10})
  
      assert len(X_train) <= 5
      in_colors = ["blue", "green", "purple", "brown", "black"]
      out_colors = ["red", "orange", "grey", "violet", "pink"]
      plt.figure(figsize=(13, 10))
      plt.suptitle("Demo of Generating Data in Clusters", fontsize=15)
      for i, cluster in enumerate(X_train):
          X_train_outliers, X_train_inliers = get_outliers_inliers(
              cluster, y_train[i]
          )
          _plot(
              X_train_inliers,
              X_train_outliers,
              inlier_color=in_colors[i],
              outlier_color=out_colors[i],
          )
  
      if save_figure:
          plt.savefig()
  
      if show_figure:
          plt.show()
  
imports: from __future__ import division
  from __future__ import print_function
  import matplotlib.pyplot
  from .data import check_consistent_shape
  from .data import get_outliers_inliers
label_desc: Use image node_id 2 for calling a global function with example usage: data_visualize(X_train, y_train, show_figure, save_figure) without return types