def load_vectors(self, vectors, **kwargs):
        """
        Arguments:
            vectors: one of or a list containing instantiations of the
                GloVe, CharNGram, or Vectors classes. Alternatively, one
                of or a list of available pretrained vectors:
                charngram.100d
                fasttext.en.300d
                fasttext.simple.300d
                glove.42B.300d
                glove.840B.300d
                glove.twitter.27B.25d
                glove.twitter.27B.50d
                glove.twitter.27B.100d
                glove.twitter.27B.200d
                glove.6B.50d
                glove.6B.100d
                glove.6B.200d
                glove.6B.300d
            Remaining keyword arguments: Passed to the constructor of Vectors classes.
        """
        if not isinstance(vectors, list):
            vectors = [vectors]
        for idx, vector in enumerate(vectors):
            if six.PY2 and isinstance(vector, str):
                vector = six.text_type(vector)
            if isinstance(vector, six.string_types):
                # Convert the string pretrained vector identifier
                # to a Vectors object
                if vector not in pretrained_aliases:
                    raise ValueError(
                        "Got string input vector {}, but allowed pretrained "
                        "vectors are {}".format(
                            vector, list(pretrained_aliases.keys())))
                vectors[idx] = pretrained_aliases[vector](**kwargs)
            elif not isinstance(vector, Vectors):
                raise ValueError(
                    "Got input vectors of type {}, expected str or "
                    "Vectors object".format(type(vector)))

        tot_dim = sum(v.dim for v in vectors)
        self.vectors = torch.Tensor(len(self), tot_dim)
        for i, token in enumerate(self.itos):
            start_dim = 0
            for v in vectors:
                end_dim = start_dim + v.dim
                self.vectors[i][start_dim:end_dim] = v[token.strip()]
                start_dim = end_dim
            assert(start_dim == tot_dim)