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)

def transform(self, sents):
        """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)
        """

        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