$alert-padding-x;\n    color: inherit;\n  }\n}\n\n\n// Alternate styles\n//\n// Generate contextual modifier classes for colorizing the alert.\n\n@each $color, $value in $theme-colors {\n  .alert-#{$color} {\n    @include alert-variant(theme-color-level($color, $alert-bg-level), theme-color-level($color, $alert-border-level), theme-color-level($color, $alert-color-level));\n  }\n}\n","@mixin alert-variant($background, $border, $color) {\n  color: $color;\n  @include gradient-bg($background);\n  border-color:...

CLASS_NAME_FOOTER_MD_FIXED = 'layout-md-footer-fixed'\nconst CLASS_NAME_FOOTER_LG_FIXED = 'layout-lg-footer-fixed'\nconst CLASS_NAME_FOOTER_XL_FIXED = 'layout-xl-footer-fixed'\n\nconst Default = {\n  controlsidebarSlide: true,\n  scrollbarTheme: 'os-theme-light',\n  scrollbarAutoHide: 'l',\n  target: SELECTOR_CONTROL_SIDEBAR,\n  animationSpeed: 300\n}\n\n/**\n * Class Definition\n * ====================================================\n */\n\nclass ControlSidebar {\n  constructor(element, config) {\n...

* **`200 - 299`** are for "Successful" responses. These are the ones you would use the most.
    * `200` is the default status code, which means everything was "OK".
    * Another example would be `201`, "Created". It is commonly used after creating a new record in the database.

That's what makes it possible to have multiple automatic interactive documentation interfaces, code generation, etc.

OpenAPI has a way to define multiple security "schemes".

By using them, you can take advantage of all these standard-based tools, including these interactive documentation systems.

OpenAPI defines the following security schemes:

* `apiKey`: an application specific key that can come from:
    * A query parameter.
    * A header.

There are **tests for both Pydantic v1 and v2**, and test **coverage** is kept at **100%**.

### Changes

* There are **new parameter** fields supported by Pydantic `Field()` for:

    * `Path()`
    * `Query()`
    * `Header()`
    * `Cookie()`

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence

### List of English terms and their preferred French translations

Below is a list of English terms and their preferred French translations, separated by a colon («:»). Use these translations, do not use your own. If an existing translation does not use these terms, update it to use them.

* «/// note | Technical Details»: «/// note | Détails techniques»
* «/// note»: «/// note | Remarque»
* «/// tip»: «/// tip | Astuce»

{% endif %}

{% endfor %}

</div>

There are hundreds of other contributors, you can see them all in the <a href="https://github.com/fastapi/fastapi/graphs/contributors" class="external-link" target="_blank">FastAPI GitHub Contributors page</a>. 👷

## Top Translators

These are the **Top Translators**. 🌐

<img src="/img/async/parallel-burgers/parallel-burgers-04.png" class="illustration">

Then your cashier/cook 👨‍🍳 finally comes back with your burgers, after a long time waiting 🕙 there in front of the counter.

I'll tell you a bit more about these **concepts** here, and that would hopefully give you the **intuition** you would need to decide how to deploy your API in very different environments, possibly even in **future** ones that don't exist yet.

By considering these concepts, you will be able to **evaluate and design** the best way to deploy **your own APIs**.