underflow := x.sub(y) // If the subtraction underflowed, add m. t := NewNat().set(x) t.add(m.nat) x.assign(choice(underflow), t) return x } // SubOne computes x = x - 1 mod m. // // The length of x must be the same as the modulus. func (x *Nat) SubOne(m *Modulus) *Nat { one := NewNat().ExpandFor(m) one.limbs[0] = 1 // Sub asks for x to be reduced modulo m, while SubOne doesn't, but when // y = 1, it works, and this is an internal use. return x.Sub(one, m) } // Add computes x = x + y mod m. // // The length...

underflow := x.sub(y) // If the subtraction underflowed, add m. t := NewNat().set(x) t.add(m.nat) x.assign(choice(underflow), t) return x } // SubOne computes x = x - 1 mod m. // // The length of x must be the same as the modulus. func (x *Nat) SubOne(m *Modulus) *Nat { one := NewNat().ExpandFor(m) one.limbs[0] = 1 // Sub asks for x to be reduced modulo m, while SubOne doesn't, but when // y = 1, it works, and this is an internal use. return x.Sub(one, m) } // Add computes x = x + y mod m. // // The length...

        `tf.keras.layers.EinsumDense`.
    *   Added `tf.keras.utils.audio_dataset_from_directory` utility to easily
        generate audio classification datasets from directories of `.wav` files.
    *   Added `subset="both"` support in
        `tf.keras.utils.image_dataset_from_directory`,`tf.keras.utils.text_dataset_from_directory`,
        and `audio_dataset_from_directory`, to be used with the

    * This helps to more easily solve use cases like:
        * Reading a body before and/or after a request (equivalent to a middleware).
        * Run middleware-like code only for a subset of *path operations*.
        * Process a request before passing it to a *path operation function*. E.g. decompressing, deserializing, etc.