divide 8 are more complex to implement, but // are likely to be more efficient if necessary. table := [(1 << 4) - 1]*Nat{ // table[i] = x ^ (i+1) // newNat calls are unrolled so they are allocated on the stack. NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), } table[0].set(x).montgomeryRepresenta(m) for i := 1; i < len(table); i++ { table[i].montgomeryMul(table[i-1], table[0], m) } out.resetFor(m) out.limbs[0]...

divide 8 are more complex to implement, but // are likely to be more efficient if necessary. table := [(1 << 4) - 1]*Nat{ // table[i] = x ^ (i+1) // newNat calls are unrolled so they are allocated on the stack. NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), } table[0].set(x).montgomeryRepresenta(m) for i := 1; i < len(table); i++ { table[i].montgomeryMul(table[i-1], table[0], m) } out.resetFor(m) out.limbs[0]...

*   `tf.experimental.ExtensionType`

    *   `tf.experimental.ExtensionType` now supports Python `tuple` as the type annotation of its fields.

*   `tf.nest`

    *   Deprecated API `tf.nest.is_sequence` has now been deleted. Please use `tf.nest.is_nested` instead.

## Keras

Keras is a framework built on top of the TensorFlow. See more details on the [Keras website](https://keras.io/).

### Breaking Changes

- Kubeadm: upgrade supports fallback to the nearest known etcd version if an unknown k8s version is passed ([#88373](https://github.com/kubernetes/kubernetes/pull/88373), [@SataQiu](https://github.com/SataQiu)) [SIG Cluster Lifecycle]