"true" or "1", this environment variable makes `tf.nn.bias_add` operate
    deterministically (i.e. reproducibly), but currently only when XLA JIT
    compilation is *not* enabled. Setting `TF_DETERMINISTIC_OPS` to "true" or
    "1" also makes cuDNN convolution and max-pooling operate deterministically.
    This makes Keras Conv\*D and MaxPool\*D layers operate deterministically in

Montgomery multiplication by a value out of the Montgomery domain // takes the result out of Montgomery representation. xR := NewNat().set(x).montgomeryRepresenta(m) // xR = x * R mod m return x.montgomeryMul(xR, y, m) // x = xR * y / R mod m } n := len(m.nat.limbs) xLimbs := x.limbs[:n] yLimbs := y.limbs[:n] switch n { default: // Attempt to use a stack-allocated backing array. T := make([]uint, 0, preallocLimbs*2) if cap(T) < n*2 { T = make([]uint, 0, n*2) } T = T[:n*2] // T = x * y for i := 0; i < n;...

failed the check // (but not the value or sign of the coefficient). y := make([]ringElement, l, maxL) for r := range y { counter := make([]byte, 2) byteorder.LEPutUint16(counter, uint16(κ)) κ++ H.Reset() H.Write(nonce) H.Write(counter) v := make([]byte, (p.γ1+1)*n/8, (maxγ1+1)*n/8) H.Read(v) y[r] = bitUnpack(v, p) } // w = NTT⁻¹(Â ∘ NTT(y)) yHat := make([]nttElement, l, maxL) for i := range y { yHat[i] = ntt(y[i]) } w := make([]ringElement, k, maxK) for i := range w { var wHat nttElement for j :=...

Setting options in `include_router` still makes sense in some cases, for example, to override or increase configurations from a third party router included in an app. But in a router that is part of a bigger application, it would probably make more sense to add those settings when creating the `APIRouter`.

**In `FastAPI`**