Consider changing `BroadcastTo` to allow its shape to be derived from another symbol

[arcondello]

Say you want to do

x = model.set(5).reshape(-1, 1)
y = model.constant(np.arange(3).reshape(1, 3))
z = x + y

this should be valid because the resulting z should have shape (-1, 3). But unfortunately our current BinaryOp implementation does not support non-scalar broadcasting and our BroadcastTo symbol does not support getting the desired shape from another symbol.

We should change the latter. Something like

xb = broadcast_to(x, shape=(-1, 3))  # we can do this now
yb = broadcast_to(y, shape=x)  # this saying `yb` should have the shape that would result from broadcasting `y` and `x`

[arcondello]

Thinking about this more, I don't think we want BroadcastTo because np.broadcast_to() accepts the specific shape we wish to broadcast to, not the shape that we're broadcasting with.

So maybe something like a BroadcastBy symbol?

Or maybe we let the BroadcastTo symbol node use the broadcast with semantics but limit dwave.optimization.broadcast_to() function to the same semantics as NumPy. Probably would mean we should rename BroadcastTo symbol. for clarity.

IMO a separate symbol probably is better/cleaner, but needs more thought.