# Response Cookies { #response-cookies }

## Use a `Response` parameter { #use-a-response-parameter }

You can declare a parameter of type `Response` in your *path operation function*.

And then you can set cookies in that *temporal* response object.

{* ../../docs_src/response_cookies/tutorial002_py310.py hl[1, 8:9] *}

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

$ 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`...

You can return a `RedirectResponse` directly:

{* ../../docs_src/custom_response/tutorial006_py310.py hl[2,9] *}

---

Or you can use it in the `response_class` parameter:


{* ../../docs_src/custom_response/tutorial006b_py310.py hl[2,7,9] *}

If you do that, then you can return the URL directly from your *path operation* function.

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

```python
from typing import Any


def some_function(data: Any):
    print(data)
```

### Generic types { #generic-types }

Some types can take "type parameters" in square brackets, to define their internal types, for example a "list of strings" would be declared `list[str]`.

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).

# Configure Swagger UI { #configure-swagger-ui }

You can configure some extra [Swagger UI parameters](https://swagger.io/docs/open-source-tools/swagger-ui/usage/configuration/).

To configure them, pass the `swagger_ui_parameters` argument when creating the `FastAPI()` app object or to the `get_swagger_ui_html()` function.

`swagger_ui_parameters` receives a dictionary with the configurations passed to Swagger UI directly.

///

## Learn More { #learn-more }

You can learn more about **GraphQL** in the [official GraphQL documentation](https://graphql.org/).

It is very helpful if you can prepare a reproduction of the bug. In other words, provide a small test case which we can run to confirm your bug. It makes it easier to find the problem and to fix it. Test cases should be provided as `curl` commands which we can copy and paste into a terminal to run it locally, for example:

```sh
# delete the index