code: def set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_):
          
          self.vectors = torch.Tensor(len(self), dim)
          for i, token in enumerate(self.itos):
              wv_index = stoi.get(token, None)
              if wv_index is not None:
                  self.vectors[i] = vectors[wv_index]
              else:
                  self.vectors[i] = unk_init(self.vectors[i])
docs: Set the vectors for the Vocab instance from a collection of Tensors.
  
          Arguments:
              stoi: A dictionary of string to the index of the associated vector
                  in the `vectors` input argument.
              vectors: An indexed iterable (or other structure supporting __getitem__) that
                  given an input index, returns a FloatTensor representing the vector
                  for the token associated with the index. For example,
                  vector[stoi["string"]] should return the vector for "string".
              dim: The dimensionality of the vectors.
              unk_init (callback): by default, initialize out-of-vocabulary word vectors
                  to zero vectors; can be any function that takes in a Tensor and
                  returns a Tensor of the same size. Default: torch.Tensor.zero_