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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):
 """ Renders ROC-AUC plot. Called internally by score, possibly more than once Returns ------- ax : the axis with the plotted figure """

        colors = self.colors[0:len(self.classes_)]
        n_classes = len(colors)

        # If it's a binary decision, plot the single ROC curve
        if self._binary_decision == True:
            self.ax.plot(
                self.fpr[0], self.tpr[0],
                label='ROC for binary decision, AUC = {:0.2f}'.format(
                        self.roc_auc[0]
                )
            )

        # If per-class plotting is requested, plot ROC curves for each class
        if self.per_class:
            for i, color in zip(range(n_classes), colors):
                self.ax.plot(
                    self.fpr[i], self.tpr[i], color=color,
                    label='ROC of class {}, AUC = {:0.2f}'.format(
                        self.classes_[i], self.roc_auc[i],
                    )
                )

        # If requested, plot the ROC curve for the micro average
        if self.micro:
            self.ax.plot(
                self.fpr[MICRO], self.tpr[MICRO], linestyle="--",
                color= self.colors[len(self.classes_)-1],
                label='micro-average ROC curve, AUC = {:0.2f}'.format(
                    self.roc_auc["micro"],
                )
            )

        # If requested, plot the ROC curve for the macro average
        if self.macro:
            self.ax.plot(
                self.fpr[MACRO], self.tpr[MACRO], linestyle="--",
                color= self.colors[len(self.classes_)-1],
                label='macro-average ROC curve, AUC = {:0.2f}'.format(
                    self.roc_auc["macro"],
                )
            )

        # Plot the line of no discrimination to compare the curve to.
        self.ax.plot([0,1], [0,1], linestyle=':', c=LINE_COLOR)
        return self.ax