* If you use the very rarely-used function `tf.keras.mixed_precision.experimental.get_layer_policy`:...

(xLimbs[i] ^ dLimbs[i])) xLimbs[i], carry = bits.Add(l, l, carry) dLimbs[i], borrow = bits.Sub(xLimbs[i], mLimbs[i], borrow) } // Like in maybeSubtractModulus, we need the subtraction if either it // didn't underflow (meaning 2x + b > m) or if computing 2x + b // overflowed (meaning 2x + b > 2^_W*n > m). needSubtraction = not(choice(borrow)) | choice(carry) } return x.assign(needSubtraction, d) } // Mod calculates out = x mod m. // // This works regardless how large the value of x is. // // The output...

(xLimbs[i] ^ dLimbs[i])) xLimbs[i], carry = bits.Add(l, l, carry) dLimbs[i], borrow = bits.Sub(xLimbs[i], mLimbs[i], borrow) } // Like in maybeSubtractModulus, we need the subtraction if either it // didn't underflow (meaning 2x + b > m) or if computing 2x + b // overflowed (meaning 2x + b > 2^_W*n > m). needSubtraction = not(choice(borrow)) | choice(carry) } return x.assign(needSubtraction, d) } // Mod calculates out = x mod m. // // This works regardless how large the value of x is. // // The output...