I would like to expand a 2d tensor to a 3d tensor, varying which dimension is expanded in each row of the tensor.  For example, I’d like to take this 2d tensor:
values = torch.tensor([[1, 2],
					   [3, 4],
					   [5, 6],
					   [7, 8],
					   [9, 10]])

And transform it to this 3d tensor:
tensor([[[ 1,  1],
         [ 2,  2]],

        [[ 3,  4],
         [ 3,  4]],

        [[ 5,  5],
         [ 6,  6]],

        [[ 7,  7],
         [ 8,  8]],

        [[ 9, 10],
         [ 9, 10]]])

where the dimension to expand for each row is given by these indices:
torch.tensor([0, 1, 0, 0, 1])

The following code produces the desired result, but is inefficient:
def expand_slow(expand_indices, values):
    expanded_values = torch.stack([
      row_val.expand([2,-1]).transpose(expand_indices[row_idx], 1) 
      for row_idx, row_val in enumerate(torch.unbind(values, dim=0))
                                   ], dim=0)
    return expanded_values

expand_indices = torch.tensor([0, 1, 0, 0, 1])

values = torch.tensor([[1, 2],
                       [3, 4],
                       [5, 6],
                       [7, 8],
                       [9, 10]])

expand_slow(expand_indices, values)

What’s the best way to perform this operation using efficient pytorch functions?  Thank you!