code: def show_top_losses(self, k:int, max_len:int=70)->None:
          
          from IPython.display import display, HTML
          items = []
          tl_val,tl_idx = self.top_losses()
          for i,idx in enumerate(tl_idx):
              if k <= 0: break
              k -= 1
              tx,cl = self.data.dl(self.ds_type).dataset[idx]
              cl = cl.data
              classes = self.data.classes
              txt = ' '.join(tx.text.split(' ')[:max_len]) if max_len is not None else tx.text
              tmp = [txt, f'{classes[self.pred_class[idx]]}', f'{classes[cl]}', f'{self.losses[idx]:.2f}',
                     f'{self.probs[idx][cl]:.2f}']
              items.append(tmp)
          items = np.array(items)
          names = ['Text', 'Prediction', 'Actual', 'Loss', 'Probability']
          df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
          with pd.option_context('display.max_colwidth', -1):
              display(HTML(df.to_html(index=False)))
docs: Create a tabulation showing the first `k` texts in top_losses along with their prediction, actual,loss, and probability of
          actual class. `max_len` is the maximum number of tokens displayed.