code: 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):
          
          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)
docs: 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'.