worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their

            DeclaredMemberScopeKind.COMBINED -> combinedScope
        }
    }

    /**
     * Returns a declared member scope which contains both static and non-static callables, as well as all classifiers. Java classes need to
     * be handled specially, because [declaredMemberScope] doesn't handle Java enhancement properly.
     */

It upgrades the version of Starlette to `0.15.0`, now based on [AnyIO](https://anyio.readthedocs.io/en/stable/), and the internal async components in **FastAPI** are now based on AnyIO as well, making it compatible with both **asyncio** and **Trio**.

You can read the docs about running [FastAPI with Trio using Hypercorn](https://fastapi.tiangolo.com/deployment/manually/#hypercorn-with-trio).

      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their

      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their

    *   tf.optimizers => tf.keras.optimizers
    *   Add tf.keras.layers.AbstractRNNCell as the preferred implementation of
        RNN cell for TF v2. User can use it to implement RNN cell with custom
        behavior.
    *   Adding `clear_losses` API to be able to clear losses at the end of
        forward pass in a custom training loop in eager.

    - If excluded, ignore the pod and return prevResult
1. Setup redirect rules for the pods:
    - Get the port list from pods definition, as well as annotations.
    - Setup iptables with required port list: `nsenter --net=<k8s pod netns> /opt/cni/bin/istio-iptables ...`. Following conditions will prevent the redirect rules to be setup in the pods:

## Configuration protocol

Ztunnel, of course, needs to be dynamically configured in order to make decisions on how it should handle traffic.
For this purpose, we chose to use xDS transport protocol due to our expertise and existing infrastructure, and because the protocol is well suited to our needs.

            .flatMap {
                // We must use `it.getAvailableScopes()` instead of `it.scope` to check scopes of companion objects
                // and context receivers as well.
                it.getAvailableScopes()
            }

        val result = buildList {
            addAll(nonLocalScopes)
            addIfNotNull(createFakeImportingScope(newImports))