def plot_latent_inducing(self,<EOL>which_indices=None,<EOL>legend=False,<EOL>plot_limits=None,<EOL>marker=None,<EOL>projection='<STR_LIT>',<EOL>**kwargs):
canvas, projection, kwargs, sig_dims = _new_canvas(self, projection, kwargs, which_indices)<EOL>if legend: label = '<STR_LIT>'<EOL>else: label = None<EOL>if marker is not None:<EOL><INDENT>kwargs['<STR_LIT>'] = marker<EOL><DEDENT>update_not_existing_kwargs(kwargs, pl().defaults.inducing_2d)  <EOL>from .data_plots import _plot_inducing<EOL>scatters = _plot_inducing(self, canvas, sig_dims[:<NUM_LIT:2>], projection, label, **kwargs)<EOL>return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)<EOL>
Plot a scatter plot of the inducing inputs.

:param [int] which_indices: which input dimensions to plot against each other
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param str marker: marker to use [default is custom arrow like]
:param kwargs: the kwargs for the scatter plots
:param str projection: for now 2d or 3d projection (other projections can be implemented, see developer documentation)