def plot_latent(self, labels=None, which_indices=None,<EOL>resolution=<NUM_LIT>, legend=True,<EOL>plot_limits=None,<EOL>updates=False,<EOL>kern=None, marker='<STR_LIT>',<EOL>num_samples=<NUM_LIT:1000>, projection='<STR_LIT>',<EOL>predict_kwargs={},<EOL>scatter_kwargs=None, **imshow_kwargs):
from ..plotting.gpy_plot.latent_plots import plot_latent<EOL>if "<STR_LIT>" not in predict_kwargs:<EOL><INDENT>predict_kwargs['<STR_LIT>'] = <NUM_LIT:0><EOL><DEDENT>Yindex = predict_kwargs['<STR_LIT>']<EOL>self.kern = self.bgplvms[Yindex].kern<EOL>self.likelihood = self.bgplvms[Yindex].likelihood<EOL>return plot_latent(self, labels, which_indices, resolution, legend, plot_limits, updates, kern, marker, num_samples, projection, scatter_kwargs)<EOL>
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'.