**Note**: You probably don't need `contextvars`, and you should probably avoid using them. But they are powerful and useful in some advanced scenarios, for example, migrating from code that used Flask's `g` semi-global variable.

- Cleanup subnet in frontend IP configs to prevent huge subnet request bodies in some scenarios. ([#98288](https://github.com/kubernetes/kubernetes/pull/98288), [@nilo19](https://github.com/nilo19)) [SIG Cloud Provider]

        and `tf.keras.layers.LocallyConnected1D`. The new mode
        (`implementation=2`) performs forward pass as a single dense matrix
        multiplication, allowing dramatic speedups in certain scenarios (but
        worse performance in others - see docstring). The option also allows to
        use `padding=same`.
    *   Add documentation clarifying the differences between tf.fill and
        tf.constant.

iteration Result = Composite W = 55 B = 7 # End of iteration Result = Composite W = 55 B = 23 # End of iteration Result = Composite W = 55 B = 2e # End of iteration Result = Composite W = 55 B = 2a # W below is composite, but it is one of the worst case scenarios for # Miller-Rabin, from Wycheproof tests. 1/4 of witnesses report the value is # prime. Test that we correctly classify false and true witnesses. # b^m = w-1 Result = PossiblyPrime W = 550fda19f97cdfbd1393 B = 379c6027f818b5164bc1 # b^m = w-1 Result...