torchtyping

You can do that in python using https://github.com/patrick-kidger/torchtyping looks like this: def batch_outer_product(x: TensorType["batch", "x_channels"], y: TensorType["batch", "y_channels"] ) -> TensorType["batch", "x_channels", "y_channels"]: return x.unsqueeze(-1) * y.unsqueeze(-2) There's also https://github.com/thomasahle/tensorgrad which uses sympy for "axis" dimension variables: b, x, y = sp.symbols("b x y") X = tg.Variable("X", b, x) Y = tg.Variable("Y", b, y) W = tg.Variable("W", x, y) XWmY = X @ W - Y

I disagree. I've had a serious attempt at array typing using variadic generics and I'm not impressed. Python's type system has numerous issues... and now they just apply to any "ArrayWithNDimensions" type as well as any "ArrayWith2Dimensions" type. Variadic protocols don't exist; many operations like stacking are inexpressible; the synatx is awful and verbose; etc. etc. I've written more about this here as part of my TorchTyping project: [0] [0] https://github.com/patrick-kidger/torchtyping/issues/37#issu...