This can be very useful for setting up **resources** that you need to use for the whole app, and that are **shared** among requests, and/or that you need to **clean up** afterwards. For example, a database connection pool, or loading a shared machine learning model.

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

### FastAPI Data Filtering { #fastapi-data-filtering }

Now, for FastAPI, it will see the return type and make sure that what you return includes **only** the fields that are declared in the type.

### Required, can be `None` { #required-can-be-none }

You can declare that a parameter can accept `None`, but that it's still required. This would force clients to send a value, even if the value is `None`.

To do that, you can declare that `None` is a valid type but simply do not declare a default value:

{* ../../docs_src/query_params_str_validations/tutorial006c_an_py310.py hl[9] *}

fastapi[standard]>=0.112.0,<0.113.0
```

that would mean that you would use the versions `0.112.0` or above, but less than `0.113.0`, for example, a version `0.112.2` would still be accepted.

If you use any other tool to manage your installations, like `uv`, Poetry, Pipenv, or others, they all have a way that you can use to define specific versions for your packages.

## Available versions { #available-versions }

* 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. ✅

* Many PRs don't have tests, you can **remind** them to add tests, or you can even **suggest** some tests yourself. That's one of the things that consume most time and you can help a lot with that.

* Then also comment what you tried, that way I'll know that you checked it. 🤓

OAuth2 was designed so that the backend or API could be independent of the server that authenticates the user.

But in this case, the same **FastAPI** application will handle the API and the authentication.

So, let's review it from that simplified point of view:

* The user types the `username` and `password` in the frontend, and hits `Enter`.

<img src="/img/deployment/https/https03.drawio.svg">

The client already **trusts** the entity that generated that TLS certificate (in this case Let's Encrypt, but we'll see about that later), so it can **verify** that the certificate is valid.

## Number validations: greater than and less than or equal { #number-validations-greater-than-and-less-than-or-equal }

The same applies for:

* `gt`: `g`reater `t`han
* `le`: `l`ess than or `e`qual

{* ../../docs_src/path_params_numeric_validations/tutorial005_an_py39.py hl[10] *}

## Number validations: floats, greater than and less than { #number-validations-floats-greater-than-and-less-than }

* `feature`: Features
    * New features, adding support for things that didn't exist before.
* `bug`: Fixes

available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced
variables from your model are also going to be untrusted. That means that if