docs: 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)