code: def as_tensor_dict(self,
                         padding_lengths: Dict[str, Dict[str, int]] = None) -> Dict[str, DataArray]:
          
          padding_lengths = padding_lengths or self.get_padding_lengths()
          tensors = {}
          for field_name, field in self.fields.items():
              tensors[field_name] = field.as_tensor(padding_lengths[field_name])
          return tensors
docs: Pads each ``Field`` in this instance to the lengths given in ``padding_lengths`` (which is
          keyed by field name, then by padding key, the same as the return value in
          :func:`get_padding_lengths`), returning a list of torch tensors for each field.
  
          If ``padding_lengths`` is omitted, we will call ``self.get_padding_lengths()`` to get the
          sizes of the tensors to create.