def _cl_int_plot_top_losses(self, k, largest=True, figsize=(12,12), heatmap:bool=True, heatmap_thresh:int=16,
                            return_fig:bool=None)->Optional[plt.Figure]:
    "Show images in `top_losses` along with their prediction, actual, loss, and probability of actual class."
    tl_val,tl_idx = self.top_losses(k, largest)
    classes = self.data.classes
    cols = math.ceil(math.sqrt(k))
    rows = math.ceil(k/cols)
    fig,axes = plt.subplots(rows, cols, figsize=figsize)
    fig.suptitle('prediction/actual/loss/probability', weight='bold', size=14)
    for i,idx in enumerate(tl_idx):
        im,cl = self.data.dl(self.ds_type).dataset[idx]
        cl = int(cl)
        im.show(ax=axes.flat[i], title=
            f'{classes[self.pred_class[idx]]}/{classes[cl]} / {self.losses[idx]:.2f} / {self.probs[idx][cl]:.2f}')
        if heatmap:
            xb,_ = self.data.one_item(im, detach=False, denorm=False)
            m = self.learn.model.eval()
            with hook_output(m[0]) as hook_a:
                with hook_output(m[0], grad= True) as hook_g:
                    preds = m(xb)
                    preds[0,cl].backward()
            acts = hook_a.stored[0].cpu()
            if (acts.shape[-1]*acts.shape[-2]) >= heatmap_thresh:
                grad = hook_g.stored[0][0].cpu()
                grad_chan = grad.mean(1).mean(1)
                mult = F.relu(((acts*grad_chan[...,None,None])).sum(0))
                sz = list(im.shape[-2:])
                axes.flat[i].imshow(mult, alpha=0.6, extent=(0,*sz[::-1],0), interpolation='bilinear', cmap='magma')                
    if ifnone(return_fig, defaults.return_fig): return fig