code: def combine_tensors(combination: str, tensors: List[torch.Tensor]) -> torch.Tensor:
      
      if len(tensors) > 9:
          raise ConfigurationError("Double-digit tensor lists not currently supported")
      combination = combination.replace('x', '1').replace('y', '2')
      to_concatenate = [_get_combination(piece, tensors) for piece in combination.split(',')]
      return torch.cat(to_concatenate, dim=-1)
docs: Combines a list of tensors using element-wise operations and concatenation, specified by a
      ``combination`` string.  The string refers to (1-indexed) positions in the input tensor list,
      and looks like ``"1,2,1+2,3-1"``.
  
      We allow the following kinds of combinations: ``x``, ``x*y``, ``x+y``, ``x-y``, and ``x/y``,
      where ``x`` and ``y`` are positive integers less than or equal to ``len(tensors)``.  Each of
      the binary operations is performed elementwise.  You can give as many combinations as you want
      in the ``combination`` string.  For example, for the input string ``"1,2,1*2"``, the result
      would be ``[1;2;1*2]``, as you would expect, where ``[;]`` is concatenation along the last
      dimension.
  
      If you have a fixed, known way to combine tensors that you use in a model, you should probably
      just use something like ``torch.cat([x_tensor, y_tensor, x_tensor * y_tensor])``.  This
      function adds some complexity that is only necessary if you want the specific combination used
      to be `configurable`.
  
      If you want to do any element-wise operations, the tensors involved in each element-wise
      operation must have the same shape.
  
      This function also accepts ``x`` and ``y`` in place of ``1`` and ``2`` in the combination
      string.