large the value of x is. // // The output will be resized to the size of m and overwritten. // //go:norace func (out *Nat) Mod(x *Nat, m *Modulus) *Nat { out.resetFor(m) // Working our way from the most significant to the least significant limb, // we can insert each limb at the least significant position, shifting all // previous limbs left by _W. This way each limb will get shifted by the // correct number of bits. We can insert at least N - 1 limbs without // overflowing m. After that, we need...

large the value of x is. // // The output will be resized to the size of m and overwritten. // //go:norace func (out *Nat) Mod(x *Nat, m *Modulus) *Nat { out.resetFor(m) // Working our way from the most significant to the least significant limb, // we can insert each limb at the least significant position, shifting all // previous limbs left by _W. This way each limb will get shifted by the // correct number of bits. We can insert at least N - 1 limbs without // overflowing m. After that, we need...

* `tf.keras`
    * `Model.compile` now support `steps_per_execution='auto'` as a parameter, allowing automatic tuning of steps per execution during `Model.fit`,
    `Model.predict`, and `Model.evaluate` for a significant performance boost.

## Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

### Ingress graduates to General Availability