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

* 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. 🤓

## Create a Pull Request { #create-a-pull-request }

You can use the same dependency *functions* you use normally.

### Dependency requirements { #dependency-requirements }

They can declare request requirements (like headers) or other sub-dependencies:

{* ../../docs_src/dependencies/tutorial006_an_py310.py hl[8,13] *}

### Raise exceptions { #raise-exceptions }

These dependencies can `raise` exceptions, the same as normal dependencies:

      value: >
        Comparing two approaches to a use case side by side can make it easier to examine the
        differences between them.


        Additionally, it's very useful to us if you can provide a "straw API" — what the
        method signatures would look like, for example, even if the method and class names are still
        in flux. This can make the feature you're suggesting much clearer to us.

  - type: textarea

        emptyGenerator = new EmptyGenerator();

        // Note: isTarget method requires FessConfig from ComponentUtil
        // which needs container initialization.
        // We can only test that the method can be called without crashing
        // when proper conditions are not met.

        Map<String, Object> docMap = new HashMap<>();

        // This will return false because getFessConfig() will fail