*   Losses are scaled in canned estimator v2 and not in the optimizers
        anymore. If you are using Estimator + distribution strategy + optimikzer
        v1 then the behavior does not change. This implies that if you are using
        custom estimator with optimizer v2, you have to scale losses. We have
        new utilities to help scale losses `tf.nn.compute_average_loss`,
        `tf.nn.scale_regularization_loss`.

errors.New("crypto/rsa: invalid prime") } if pN.Mul(qN, N).IsZero() != 1 { return errors.New("crypto/rsa: p * q != n") } // Check that de ≡ 1 mod p-1, and de ≡ 1 mod q-1. // // This implies that e is coprime to each p-1 as e has a multiplicative // inverse. Therefore e is coprime to lcm(p-1,q-1) = λ(N). // It also implies that a^de ≡ a mod p as a^(p-1) ≡ 1 mod p. Thus a^de ≡ a // mod n for all a coprime to n, as required. // // This checks dP, dQ, and e. pMinus1, err := bigmod.NewModulus(p.Nat().SubOne(p).Bytes(p))...

errors.New("crypto/rsa: invalid prime") } if pN.Mul(qN, N).IsZero() != 1 { return errors.New("crypto/rsa: p * q != n") } // Check that de ≡ 1 mod p-1, and de ≡ 1 mod q-1. // // This implies that e is coprime to each p-1 as e has a multiplicative // inverse. Therefore e is coprime to lcm(p-1,q-1) = λ(N). // It also implies that a^de ≡ a mod p as a^(p-1) ≡ 1 mod p. Thus a^de ≡ a // mod n for all a coprime to n, as required. // // This checks dP, dQ, and e. We don't check d because it is not actually //...