def qmul(q, r):
    """
    Multiply quaternion(s) q with quaternion(s) r.
    Expects two equally-sized tensors of shape (*, 4), where * denotes any number of dimensions.
    Returns q*r as a tensor of shape (*, 4).
    """
    assert q.shape[-1] == 4
    assert r.shape[-1] == 4

    original_shape = q.shape

    # Compute outer product
    terms = torch.bmm(r.view(-1, 4, 1), q.view(-1, 1, 4))

    w = terms[:, 0, 0] - terms[:, 1, 1] - terms[:, 2, 2] - terms[:, 3, 3]
    x = terms[:, 0, 1] + terms[:, 1, 0] - terms[:, 2, 3] + terms[:, 3, 2]
    y = terms[:, 0, 2] + terms[:, 1, 3] + terms[:, 2, 0] - terms[:, 3, 1]
    z = terms[:, 0, 3] - terms[:, 1, 2] + terms[:, 2, 1] + terms[:, 3, 0]
    return torch.stack((w, x, y, z), dim=1).view(original_shape)

    Multiply quaternion(s) q with quaternion(s) r.
    Expects two equally-sized tensors of shape (*, 4), where * denotes any number of dimensions.
    Returns q*r as a tensor of shape (*, 4).
    
mGPT/data/humanml/common/quaternion.py