code: def plot_confusion_matrix_with_cv(clf, X, y, labels=None, true_labels=None,
                                    pred_labels=None, title=None,
                                    normalize=False, hide_zeros=False,
                                    x_tick_rotation=0, do_cv=True, cv=None,
                                    shuffle=True, random_state=None, ax=None,
                                    figsize=None, cmap='Blues',
                                    title_fontsize="large",
                                    text_fontsize="medium"):
      y = np.array(y)
      if not do_cv:
          y_pred = clf.predict(X)
          y_true = y
      else:
          if cv is None:
              cv = StratifiedKFold(shuffle=shuffle, random_state=random_state)
          elif isinstance(cv, int):
              cv = StratifiedKFold(n_splits=cv, shuffle=shuffle,
                                   random_state=random_state)
          else:
              pass
          clf_clone = clone(clf)
          preds_list = []
          trues_list = []
          for train_index, test_index in cv.split(X, y):
              X_train, X_test = X[train_index], X[test_index]
              y_train, y_test = y[train_index], y[test_index]
              clf_clone.fit(X_train, y_train)
              preds = clf_clone.predict(X_test)
              preds_list.append(preds)
              trues_list.append(y_test)
          y_pred = np.concatenate(preds_list)
          y_true = np.concatenate(trues_list)
      ax = plotters.plot_confusion_matrix(y_true=y_true, y_pred=y_pred,
                                          labels=labels, true_labels=true_labels,
                                          pred_labels=pred_labels,
                                          title=title, normalize=normalize,
                                          hide_zeros=hide_zeros,
                                          x_tick_rotation=x_tick_rotation, ax=ax,
                                          figsize=figsize, cmap=cmap,
                                          title_fontsize=title_fontsize,
                                          text_fontsize=text_fontsize)
      return ax