*   tf.optimizers => tf.keras.optimizers
    *   Add tf.keras.layers.AbstractRNNCell as the preferred implementation of
        RNN cell for TF v2. User can use it to implement RNN cell with custom
        behavior.
    *   Adding `clear_losses` API to be able to clear losses at the end of
        forward pass in a custom training loop in eager.

{ c := compress(a, bits) if c >= 1<<bits { t.Fatalf("compress(%d, %d) = %d >= 2^bits", a, bits, c) } got := decompress(c, bits) diff := min(a-got, got-a, a-got+q, got-a+q) ceil := q / (1 << bits) if diff > fieldElement(ceil) { t.Fatalf("decompress(compress(%d, %d), %d) = %d (diff %d, max diff %d)", a, bits, bits, got, diff, ceil) } } } } func CompressRat(x fieldElement, d uint8) uint16 { if x >= q { panic("x out of range") } if d <= 0 || d >= 12 { panic("d out of range") } precise := big.NewRat((1<<d)*int64(x),...

{ c := compress(a, bits) if c >= 1<<bits { t.Fatalf("compress(%d, %d) = %d >= 2^bits", a, bits, c) } got := decompress(c, bits) diff := min(a-got, got-a, a-got+q, got-a+q) ceil := q / (1 << bits) if diff > fieldElement(ceil) { t.Fatalf("decompress(compress(%d, %d), %d) = %d (diff %d, max diff %d)", a, bits, bits, got, diff, ceil) } } } } func CompressRat(x fieldElement, d uint8) uint16 { if x >= q { panic("x out of range") } if d <= 0 || d >= 12 { panic("d out of range") } precise := big.NewRat((1<<d)*int64(x),...