Is there an obvious trick to working around the limitation of broadcasting on Sparse matrices?

Take for example:

```
a = torch.rand((4)).to_sparse()
b = torch.rand((4, 4)).to_sparse()
a * b
```

which throws:

```
RuntimeError: sparse_binary_op_intersection_cpu(): expects sparse inputs with equal dimensionality, number of sparse dimensions, and shape of sparse dimensions
```

I can’t use `to_dense()`

as these are massive tensors, and I also do need `require_grad`

with respect to `a`

. Any workaround?

Thanks!