<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:

def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
             **kwargs)->Optional[plt.Figure]:
        "Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient"
        lrs = self._split_list(self.lrs, skip_start, skip_end)
        losses = self._split_list(self.losses, skip_start, skip_end)
        losses = [x.item() for x in losses]
        if 'k' in kwargs: losses = self.smoothen_by_spline(lrs, losses, **kwargs)
        fig, ax = plt.subplots(1,1)
        ax.plot(lrs, losses)
        ax.set_ylabel("Loss")
        ax.set_xlabel("Learning Rate")
        ax.set_xscale('log')
        ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0e'))
        if suggestion:
            try: mg = (np.gradient(np.array(losses))).argmin()
            except:
                print("Failed to compute the gradients, there might not be enough points.")
                return
            print(f"Min numerical gradient: {lrs[mg]:.2E}")
            ax.plot(lrs[mg],losses[mg],markersize=10,marker='o',color='red')
            self.min_grad_lr = lrs[mg]
        if ifnone(return_fig, defaults.return_fig): return fig
        if not IN_NOTEBOOK: plot_sixel(fig)