* an expressive source hint for the model problem. Instead, the source hint is configured by the model builder before
 * it delegates to other components that potentially encounter problems. Then, the problem reporter can focus on
 * providing a simple error message, leaving the donkey work of creating a nice model problem to this component.
 *
 */
public interface ModelProblemCollector {

    /**
     * The collected problems.

 * an expressive source hint for the model problem. Instead, the source hint is configured by the model builder before
 * it delegates to other components that potentially encounter problems. Then, the problem reporter can focus on
 * providing a simple error message, leaving the donkey work of creating a nice model problem to this component.
 *
 */
class DefaultModelProblemCollector implements ModelProblemCollector {

## First Steps

Let's see a very simple example. It will be so simple that it is not very useful, for now.

But this way we can focus on how the **Dependency Injection** system works.

### Create a dependency, or "dependable"

Let's first focus on the dependency.

It is just a function that can take all the same parameters that a *path operation function* can take:

=== "Python 3.10+"

But then, the project's focus shifted.

It was no longer an API web framework, as the creator needed to focus on Starlette.

Now APIStar is a set of tools to validate OpenAPI specifications, not a web framework.

!!! info

* If the PR can be simplified in a way, you can ask for that, but there's no need to be too picky, there might be a lot of subjective points of view (and I will have my own as well 🙈), so it's better if you can focus on the fundamental things.

### Tests

* Help me check that the PR has **tests**.

* Check that the tests **fail** before the PR. 🚨

* Then check that the tests **pass** after the PR. ✅

    So let's focus on the main cases, where it could crash entirely in some particular cases **in the future**, and it still makes sense to restart it.

!!! tip
    In the next chapter, you will see a real secure implementation, with password hashing and <abbr title="JSON Web Tokens">JWT</abbr> tokens.

    But for now, let's focus on the specific details we need.

=== "Python 3.10+"

    ```Python hl_lines="87"
    {!> ../../../docs_src/security/tutorial003_an_py310.py!}
    ```

=== "Python 3.9+"

* HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

## HTTPS

If we focus just on the **container image** for a FastAPI application (and later the running **container**), HTTPS normally would be handled **externally** by another tool.

executing **your own code**, and particularly, **waiting for the network**. But you will probably still get some nice performance improvements just from the upgrade.

The focus of this release is **compatibility** with Pydantic v1 and v2, to make sure your current apps keep working. Later there will be more focus on refactors, correctness, code improvements, and then **performance** improvements. Some third-party early beta testers that ran benchmarks on the beta releases of FastAPI reported...

# Release 2.17.0

## TensorFlow

<INSERT SMALL BLURB ABOUT RELEASE FOCUS AREA AND POTENTIAL TOOLCHAIN CHANGES>

### Breaking Changes

* <DOCUMENT BREAKING CHANGES HERE>
* <THIS SECTION SHOULD CONTAIN API, ABI AND BEHAVIORAL BREAKING CHANGES>

### Known Caveats

* <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).>
* <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE>
* <KNOWN LACK OF SUPPORT ON SOME PLATFORM, SHOULD GO HERE>