We are not restricted to having only one dependency that can return that type of data.

///

## Other models { #other-models }

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

}
```

## Additional media types for the main response { #additional-media-types-for-the-main-response }

You can use this same `responses` parameter to add different media types for the same main response.

For example, you can add an additional media type of `image/png`, declaring that your *path operation* can return a JSON object (with media type `application/json`) or a PNG image:

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized

If there is no discussion for that language, you can start by requesting the new language. For that, you can follow these steps:

* Create a new discussion following the template.
* Get a few native speakers to comment on the discussion and commit to help review translations for that language.

Once there are several people in the discussion, the FastAPI team can evaluate it and can make it an official translation.

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

### Include the custom docs { #include-the-custom-docs }

Now you can create the *path operations* for the custom docs.

You can reuse FastAPI's internal functions to create the HTML pages for the docs, and pass them the needed arguments:

* `openapi_url`: the URL where the HTML page for the docs can get the OpenAPI schema for your API. You can use here the attribute `app.openapi_url`.
* `title`: the title of your API.

   * occur when using {@link CacheBuilder#weakKeys}, {@link CacheBuilder#weakValues}, or {@link
   * CacheBuilder#softValues}.
   */
  COLLECTED {
    @Override
    boolean wasEvicted() {
      return true;
    }
  },

  /**
   * The entry's expiration timestamp has passed. This can occur when using {@link

## Check the Docs { #check-the-docs }

You can see the query parameters in the docs UI at `/docs`:

<div class="screenshot">
<img src="/img/tutorial/query-param-models/image01.png">
</div>

## Forbid Extra Query Parameters { #forbid-extra-query-parameters }

In some special use cases (probably not very common), you might want to **restrict** the query parameters that you want to receive.

You can help with everything on [Help FastAPI - Get Help](./help-fastapi.md){.internal-link target=_blank} the same ways as external contributors. But additionally, there are some tasks that only you (as part of the team) can perform.

Here are the general instructions for the tasks you can perform.

Thanks a lot for your help. 🙇

## Be Nice

First of all, be nice. 😊

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference so you can come back and see exactly what you need.

## Run the code { #run-the-code }

All the code blocks can be copied and used directly (they are actually tested Python files).