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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 plot_fitspace(self, name: str, X: np.ndarray, y: np.ndarray, clf: object) -> None: """ Plot 2dplane of fitspace """

        cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
        cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, m_max]x[y_min, y_max].
        h = 0.01  # Mesh step size
        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                             np.arange(y_min, y_max, h))
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.figure()
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

        # Plot also the training points
        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())
        plt.xlabel(r'$\log_{10}$ Noise')
        plt.ylabel(r'$\log_{10}$ Curvature')
        plt.savefig(name)