And under the hood, Pydantic uses the same underlying Rust mechanisms as `orjson` to serialize to JSON, so you will already get the best performance with a response model.

## Default response class { #default-response-class }

It gives you all the flexibility to choose the ones that fit your project the best.

And you can use directly many well maintained and widely used packages like `pwdlib` and `PyJWT`, because **FastAPI** doesn't require any complex mechanisms to integrate external packages.

But it provides you the tools to simplify the process as much as possible without compromising flexibility, robustness, or security.

Colons are permitted as part of a bracketed IPv6 address, such as `http://[::1]/`.

Go 1.26 enabled two additional post-quantum key exchange mechanisms:
SecP256r1MLKEM768 and SecP384r1MLKEM1024. The default can be reverted using the
[`tlssecpmlkem` setting](/pkg/crypto/tls/#Config.CurvePreferences).

### One Load Balancer - Multiple Worker Containers { #one-load-balancer-multiple-worker-containers }

When working with **Kubernetes** or similar distributed container management systems, using their internal networking mechanisms would allow the single **load balancer** that is listening on the main **port** to transmit communication (requests) to possibly **multiple containers** running your app.

    deps = select({
        "//conditions:default": ["@com_github_grpc_grpc//:grpc++"],
    }),
)
# copybara:comment_end

# A shared object which includes registration mechanisms for ops and
# kernels. Does not include the implementations of any ops or kernels. Instead,
# the library which loads libtensorflow_framework.so
# (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C

*   Pins `numpy` to 1.18.5 to prevent ABI breakage when compiling code that uses
    both NumPy and TensorFlow headers.

# Release 2.3.0

## Major Features and Improvements

*   `tf.data` adds two new mechanisms to solve input pipeline bottlenecks and
    save resources:

    *   [snapshot](https://www.tensorflow.org/api_docs/python/tf/data/experimental/snapshot)

### Technical Details: `root_path` { #technical-details-root-path }

When you mount a sub-application as described above, FastAPI will take care of communicating the mount path for the sub-application using a mechanism from the ASGI specification called a `root_path`.

That way, the sub-application will know to use that path prefix for the docs UI.

### Technische Details: `root_path` { #technical-details-root-path }

Wenn Sie eine Unteranwendung wie oben beschrieben mounten, kümmert sich FastAPI darum, den Mount-Pfad für die Unteranwendung zu kommunizieren, mithilfe eines Mechanismus aus der ASGI-Spezifikation namens `root_path`.

Auf diese Weise weiß die Unteranwendung, dass sie dieses Pfadpräfix für die Benutzeroberfläche der Dokumentation verwenden soll.

Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.
This is due to Java's use of reflection and the need to maintain a lot of metadata.

- **Size of Serialized Data:**

 * which has a method {@code getParents()} that retrieves its predecessors in a graph:
 *
 * {@snippet :
 * someGraphAlgorithm(startNode, MyNode::getParents);
 * }
 *
 * <p>If you have some other mechanism for returning the predecessors of a node, or one that doesn't
 * return a {@code Iterable<? extends N>}, then you can use a lambda to perform a more general
 * transformation:
 *
 * {@snippet :