*   The new backend is used for 8 bits full integer post-training
            quantization
        *   The new backend removes the redundant rescales and fixes some bugs
            (shared weight/bias, extremely small scales, etc)
        *   Set `experimental_new_quantizer` in tf.lite.TFLiteConverter to False
            to disable this change
*   `tf.keras`
    *   `tf.keras.metrics.AUC` now support logit predictions.

* Remove gender bias in docs for handling errors. PR [#780](https://github.com/tiangolo/fastapi/pull/780). Original idea in PR [#761](https://github.com/tiangolo/fastapi/pull/761) by [@classywhetten](https://github.com/classywhetten).

correct range and to match the specification. It's unfortunate // that we need to do a shift instead of a mask, but see the comment on // rightShift. // // These are the most dangerous lines in the package and maybe in the // library: a single bit of bias in the selection of nonces would likely // lead to key recovery, but no tests would fail. Look but DO NOT TOUCH. if excess := len(b)*8 - c.N.BitLen(); excess > 0 { // Just to be safe, assert that this only happens for the one curve that // doesn't...

correct range and to match the specification. It's unfortunate // that we need to do a shift instead of a mask, but see the comment on // rightShift. // // These are the most dangerous lines in the package and maybe in the // library: a single bit of bias in the selection of nonces would likely // lead to key recovery, but no tests would fail. Look but DO NOT TOUCH. if excess := len(b)*8 - c.N.BitLen(); excess > 0 { // Just to be safe, assert that this only happens for the one curve that // doesn't...