def plot_latent(self, labels=None, which_indices=None,
                resolution=60, legend=True,
                plot_limits=None,
                updates=False,
                kern=None, marker='<>^vsd',
                num_samples=1000, projection='2d',
                predict_kwargs={},
                scatter_kwargs=None, **imshow_kwargs):
        """
        see plotting.matplot_dep.dim_reduction_plots.plot_latent
        if predict_kwargs is None, will plot latent spaces for 0th dataset (and kernel), otherwise give
        predict_kwargs=dict(Yindex='index') for plotting only the latent space of dataset with 'index'.
        """
        from ..plotting.gpy_plot.latent_plots import plot_latent

        if "Yindex" not in predict_kwargs:
            predict_kwargs['Yindex'] = 0

        Yindex = predict_kwargs['Yindex']

        self.kern = self.bgplvms[Yindex].kern
        self.likelihood = self.bgplvms[Yindex].likelihood

        return plot_latent(self, labels, which_indices, resolution, legend, plot_limits, updates, kern, marker, num_samples, projection, scatter_kwargs)