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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):
 """ Draws the cv scores as a line chart on the current axes. """

        # Set the colors from the supplied values or reasonable defaults
        color_values = resolve_colors(n_colors=4, colors=self.color)

        for idx, metric in enumerate(METRICS):
            # Skip any excluded labels
            if metric not in self.cv_scores_:
                continue

            # Get the color ensuring every metric has a static color
            color = color_values[idx]

            # Make the label pretty
            if metric == "fscore":
                if self.fbeta == 1.0:
                    label = "$f_1$"
                else:
                    label = "$f_{{\beta={:0.1f}}}".format(self.fbeta)
            else:
                label = metric.replace("_", " ")

            # Draw the metric values
            self.ax.plot(
                self.thresholds_, self.cv_scores_[metric],
                color=color, label=label
            )

            # Draw the upper and lower bounds
            lower = self.cv_scores_["{}_lower".format(metric)]
            upper = self.cv_scores_["{}_upper".format(metric)]

            self.ax.fill_between(
                self.thresholds_, upper, lower,
                alpha=0.35, linewidth=0, color=color
            )

            # Annotate the graph with the maximizing value
            if self.argmax.lower() == metric:
                argmax = self.cv_scores_[metric].argmax()
                threshold = self.thresholds_[argmax]
                self.ax.axvline(
                    threshold, ls='--', c='k', lw=1,
                    label="$t_{}={:0.2f}$".format(metric[0], threshold)
                )

        return self.ax