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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def draw(self, **kwargs):
 """ Renders the training and test learning curves. """

        # Specify the curves to draw and their labels
        labels = ("Training Score", "Cross Validation Score")
        curves = (
            (self.train_scores_mean_, self.train_scores_std_),
            (self.test_scores_mean_, self.test_scores_std_),
        )

        # Get the colors for the train and test curves
        colors = resolve_colors(n_colors=2)

        # Plot the fill betweens first so they are behind the curves.
        for idx, (mean, std) in enumerate(curves):
            # Plot one standard deviation above and below the mean
            self.ax.fill_between(
                self.train_sizes_, mean - std, mean+std, alpha=0.25,
                color=colors[idx],
            )

        # Plot the mean curves so they are in front of the variance fill
        for idx, (mean, _) in enumerate(curves):
            self.ax.plot(
                self.train_sizes_, mean, 'o-', color=colors[idx],
                label=labels[idx],
            )

        return self.ax