[mcabbott]

> until next time you have to read that

But how will this tool help? If you put the sample you used in a comment, then your confusion is wondering if that's in sync... and whether the input is like what you wrote.

IMO the sweet spot that's easy both for humans & computers is index notation. Instead of `tf.add(X, tf.expand_dims(Y, 1))` (their example, which I think also applies to 4-tensors in ways not trivial to see from the sample) something like "out_{r,c} = X_r + Y_c" is completely unambiguous, and quicker to type than making a sample.

I don't think you can do that in tf.einsum, nor einops. Is there a Python package which does this? In Julia you can write `@cast out[r,c] := X[r] + Y[c]` (with my package, but several others share this notation).

[nl]

>> until next time you have to read that

> But how will this tool help?

It's absolutely true that it doesn't help. But that's a second order problem to the hours spend trying to work out how to write the code.

It could also generate comments that explain what is happening..

[im3w1l]

I recommend

X + Y[:, tf.newaxis]

For higher dimensions of Y, you can use (but it might be ill-advised)

X + Y[:, tf.newaxis, ...]

[mcabbott]

Sure! The desire for fancier notation is from more complicated examples, the kind where you write elaborate comments to explain why you're permuting 3rd & 4th dim of B to line up with C and part of D.