def _plot_mean(self, canvas, helper_data, helper_prediction,
              levels=20, projection='2d', label=None,
              **kwargs):

    _, free_dims, Xgrid, x, y, _, _, resolution = helper_data
    if 2 <= len(free_dims):
        mu, _, _ = helper_prediction
        if len(free_dims)==1:
            # 1D plotting:
            update_not_existing_kwargs(kwargs, pl().defaults.meanplot_1d)  # @UndefinedVariable
            plots = dict(gpmean=[pl().plot(canvas, Xgrid[:, free_dims], mu, label=label, **kwargs)])
        else:
            if projection == '2d':
                update_not_existing_kwargs(kwargs, pl().defaults.meanplot_2d)  # @UndefinedVariable
                plots = dict(gpmean=[pl().contour(canvas, x[:,0], y[0,:],
                                               mu.reshape(resolution, resolution).T,
                                               levels=levels, label=label, **kwargs)])
            elif projection == '3d':
                update_not_existing_kwargs(kwargs, pl().defaults.meanplot_3d)  # @UndefinedVariable
                plots = dict(gpmean=[pl().surface(canvas, x, y,
                                               mu.reshape(resolution, resolution),
                                               label=label,
                                               **kwargs)])
    elif len(free_dims)==0:
        pass # Nothing to plot!
    else:
        raise RuntimeError('Cannot plot mean in more then 2 input dimensions')
    return plots
dataset/ETHPy150Open SheffieldML/GPy/GPy/plotting/gpy_plot/gp_plots.py/_plot_mean/swapped operands of `<=`