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:

$ python main.py

// Now it can read the environment variable

Hello Wade Wilson from Python
```

</div>

////

As environment variables can be set outside of the code, but can be read by the code, and don't have to be stored (committed to `git`) with the rest of the files, it's common to use them for configurations or **settings**.

functionality. Syntax is `mc support inspect ALIAS/path/to/files`. This can for example be used to collect `xl.meta` from objects that are misbehaving. To collect `xl.meta` from a specific object, for example placed at `ALIAS/bucket/path/to/file.txt` append `/xl.meta`, for instance `mc support inspect ALIAS/bucket/path/to/file.txt/xl.meta`. All files can be collected, so this can also be used to retrieve `part.*` files, etc.

Wildcards can be used, for example `mc support inspect ALIAS/bucket/path/**/xl.meta`...

## 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] *}

					<h4 class="modal-title">
						<la:message key="labels.crud_title_delete" />
					</h4>
					<button type="button" class="close" data-dismiss="modal"
						aria-label="Close">
						<span aria-hidden="true">×</span>
					</button>
				</div>
				<div class="modal-body">
					<p>
						<la:message key="labels.crud_delete_confirmation" />
					</p>
				</div>
				<div class="modal-footer justify-content-between">

### 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.

plugins: forked-1.1.3, xdist-1.31.0, cov-2.8.1
collected 6 items

---> 100%

test_main.py <span style="color: green; white-space: pre;">......                            [100%]</span>

<span style="color: green;">================= 1 passed in 0.03s =================</span>
```

																	<span class="badge bg-primary"><la:message
                                                                            key="labels.enabled"/></span>
                                                    </c:if> <c:if test="${data.available=='false'}">
																	<span class="badge bg-gray"><la:message

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