for RNN cells in TF v2. User can use it to implement RNN cells with
        custom behavior.
    *   Keras training and validation curves are shown on the same plot when
        using the TensorBoard callback.
    *   Switched Keras `fit/evaluate/predict` execution to use only a single
        unified path by default unless eager execution has been explicitly

    void cacheCapacityAndRetrievalOrder() {
        BufferCacheImpl impl = new BufferCacheImpl(2, 3);
        byte[] a = new byte[1];
        byte[] c = new byte[2];
        byte[] d = new byte[4];

        impl.releaseBuffer(a); // goes to slot 0
        impl.releaseBuffer(c); // goes to slot 1
        impl.releaseBuffer(d); // dropped (cache full)

In many cases you might already know the answer for those questions. 🤓

If you are helping a lot of people with their questions, you will become an official [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}. 🎉

So, to balance that out, imagine the following short story:

> You have to clean a big, dirty house.

*Yep, that's the whole story*.

---

There's no waiting 🕙 anywhere, just a lot of work to be done, on multiple places of the house.

You can use this template to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you.

In a typical modern infrastructure deployment, application, database, key-store, etc. already live in containers and are managed by orchestration platforms. MinIO brings robust, scalable, AWS S3 compatible object storage to the lot.

    assertEquals(2, copy.size());
    assertSame(copy, copy.iterator().next().member);
  }

  /*
   * The behavior of toString() and iteration is tested by LinkedHashMultiset,
   * which shares a lot of code with HashMultiset and has deterministic
   * iteration order.
   */

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

With **FastAPI**, using OpenAPI, you can define the names of these webhooks, the types of HTTP operations that your app can send (e.g. `POST`, `PUT`, etc.) and the request **bodies** that your app would send.

This can make it a lot easier for your users to **implement their APIs** to receive your **webhook** requests, they might even be able to autogenerate some of their own API code.

/// info

///

And when you return a `Response`, **FastAPI** will pass it directly.

It won't do any data conversion with Pydantic models, it won't convert the contents to any type, etc.

This gives you a lot of flexibility. You can return any data type, override any data declaration or validation, etc.

## Using the `jsonable_encoder` in a `Response` { #using-the-jsonable-encoder-in-a-response }