new_contents: print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.naive_bayes import GaussianNB
  from sklearn.svm import SVC
  from sklearn.datasets import load_digits
  from sklearn.model_selection import learning_curve
  from sklearn.model_selection import ShuffleSplit
  
  
  def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                          n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
      """
      Generate a simple plot of the test and training learning curve.
  
      Parameters
      ----------
      estimator : object type that implements the "fit" and "predict" methods
          An object of that type which is cloned for each validation.
  
      title : string
          Title for the chart.
  
      X : array-like, shape (n_samples, n_features)
          Training vector, where n_samples is the number of samples and
          n_features is the number of features.
  
      y : array-like, shape (n_samples) or (n_samples, n_features), optional
          Target relative to X for classification or regression;
          None for unsupervised learning.
  
      ylim : tuple, shape (ymin, ymax), optional
          Defines minimum and maximum yvalues plotted.
  
      cv : int, cross-validation generator or an iterable, optional
          Determines the cross-validation splitting strategy.
          Possible inputs for cv are:
            - None, to use the default 3-fold cross-validation,
            - integer, to specify the number of folds.
            - An object to be used as a cross-validation generator.
            - An iterable yielding train/test splits.
  
          For integer/None inputs, if ``y`` is binary or multiclass,
          :class:`StratifiedKFold` used. If the estimator is not a classifier
          or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
  
          Refer :ref:`User Guide <cross_validation>` for the various
          cross-validators that can be used here.
  
      n_jobs : integer, optional
          Number of jobs to run in parallel (default 1).
      """
      plt.figure()
      plt.title(title)
      if ylim is not None:
          plt.ylim(*ylim)
      plt.xlabel("Training examples")
      plt.ylabel("Score")
      train_sizes, train_scores, test_scores = learning_curve(
          estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
      train_scores_mean = np.mean(train_scores, axis=1)
      train_scores_std = np.std(train_scores, axis=1)
      test_scores_mean = np.mean(test_scores, axis=1)
      test_scores_std = np.std(test_scores, axis=1)
      plt.grid()
  
      plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                       train_scores_mean + train_scores_std, alpha=0.1,
                       color="r")
      plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                       test_scores_mean + test_scores_std, alpha=0.1, color="g")
      plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
               label="Training score")
      plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
               label="Cross-validation score")
  
      plt.legend(loc="best")
      return plt
  
subject: Check in code for printing learning curves