Plot the latent space of the GP on the inputs. This is the
    density of the GP posterior as a grey scale and the
    scatter plot of the input dimemsions selected by which_indices.

    :param array-like labels: a label for each data point (row) of the inputs
    :param (int, int) which_indices: which input dimensions to plot against each other
    :param int resolution: the resolution at which we predict the magnification factor
    :param bool legend: whether to plot the legend on the figure, if int plot legend columns on legend
    :param plot_limits: the plot limits for the plot
    :type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
    :param bool updates: if possible, make interactive updates using the specific library you are using
    :param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
    :param str marker: markers to use - cycle if more labels then markers are given
    :param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
    :param imshow_kwargs: the kwargs for the imshow (magnification factor)
    :param annotation_kwargs: the kwargs for the annotation plot
    :param scatter_kwargs: the kwargs for the scatter plots

def plot_steepest_gradient_map(self, output_labels=None, data_labels=None, which_indices=None,
                resolution=15, legend=True,
                plot_limits=None,
                updates=False,
                kern=None, marker='<>^vsd',
                num_samples=1000,
                annotation_kwargs=None, scatter_kwargs=None, **imshow_kwargs):

    """
    Plot the latent space of the GP on the inputs. This is the
    density of the GP posterior as a grey scale and the
    scatter plot of the input dimemsions selected by which_indices.

    :param array-like labels: a label for each data point (row) of the inputs
    :param (int, int) which_indices: which input dimensions to plot against each other
    :param int resolution: the resolution at which we predict the magnification factor
    :param bool legend: whether to plot the legend on the figure, if int plot legend columns on legend
    :param plot_limits: the plot limits for the plot
    :type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
    :param bool updates: if possible, make interactive updates using the specific library you are using
    :param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
    :param str marker: markers to use - cycle if more labels then markers are given
    :param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
    :param imshow_kwargs: the kwargs for the imshow (magnification factor)
    :param annotation_kwargs: the kwargs for the annotation plot
    :param scatter_kwargs: the kwargs for the scatter plots
    """
    input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
    X = get_x_y_var(self)[0]
    _, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, X, plot_limits, which_indices, None, resolution)
    canvas, imshow_kwargs = pl().new_canvas(xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]),
                           xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **imshow_kwargs)
    if (data_labels is not None):
        legend = find_best_layout_for_subplots(len(np.unique(data_labels)))[1]
    else:
        data_labels = np.ones(self.num_data)
        legend = False
    plots = dict(scatter=_plot_latent_scatter(canvas, X, which_indices, data_labels, marker, num_samples, **scatter_kwargs or {}))
    plots.update(_plot_steepest_gradient_map(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, output_labels, updates, kern, annotation_kwargs=annotation_kwargs, **imshow_kwargs))
    retval = pl().add_to_canvas(canvas, plots, legend=legend)
    _wait_for_updates(plots['annotation'], updates)
    return retval