code: def transform(self, sents):
          
  
          def convert(tokens):
              return torch.tensor([self.vocab.stoi[t] for t in tokens], dtype=torch.long)
  
          if self.vocab is None:
              raise Exception(
                  "Must run .fit() for .fit_transform() before " "calling .transform()."
              )
  
          seqs = sorted([convert(s) for s in sents], key=lambda x: -len(x))
          X = torch.LongTensor(pad_sequence(seqs, batch_first=True))
          return X
docs: Converts lists of tokens into a Tensor of embedding indices.
  
          Args:
              sents: A list of lists of tokens (representing sentences)
                  NOTE: These sentences should already be marked using the
                  mark_entities() helper.
          Returns:
              X: A Tensor of shape (num_items, max_seq_len)