Torch.jit vectorizing loops

I wrote up a simple example of matrix addition compiled as TorchScript:

def mat_add(A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
    C = torch.empty_like(A)
    for i in range(len(A)):
        for j in range(len(A[0])):
            C[i,j] = A[i,j] + B[i,j]
    return C


I thought that torch.jit.script could vectorize loops when appropriate, but the compiled graph still shows element-wise operations and thus no speedup. Is there a way to recognize and vectorize loops like these with torch.jit or some other way?