please use `optimizer._learning_rate`.
    *   **You implemented a custom optimizer based on the old optimizer.**
        Please set your optimizer to subclass
        `tf.keras.optimizer.legacy.XXX`. If you want to migrate to the new
        optimizer and find it does not support your optimizer, please file
        an issue in the Keras GitHub repo.

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 to reduce every time we shift. i := len(x.limbs) - 1 // For the first N - 1 limbs...

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 to reduce every time we shift. i := len(x.limbs) - 1 // For the first N - 1 limbs...

* ⬆️ Upgrade and re-enable installing Typer-CLI. PR [#6008](https://github.com/tiangolo/fastapi/pull/6008) by [@tiangolo](https://github.com/tiangolo).

## 0.92.0

🚨 This is a security fix. Please upgrade as soon as possible.

### Upgrades

* ⬆️ Upgrade Starlette to 0.25.0. PR [#5996](https://github.com/tiangolo/fastapi/pull/5996) by [@tiangolo](https://github.com/tiangolo).