e.g, To remove noncurrent versions of all objects keeping the most recent 5 noncurrent versions under the prefix `user-uploads/` 30 days after they become noncurrent ,

```
{
    "Rules": [
        {
            "ID": "Keep only most recent 5 noncurrent versions",
            "Status": "Enabled",
            "Filter": {
                "Prefix": "users-uploads/"
            },
            "NoncurrentVersionExpiration": {

But because **FastAPI** is based on the Python standards, including `Annotated`, you can use this trick in your code. 😎

///

The dependencies will keep working as expected, and the **best part** is that the **type information will be preserved**, which means that your editor will be able to keep providing you with **autocompletion**, **inline errors**, etc. The same for other tools like `mypy`.

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

///

### Declarar un *path parameter* { #declare-a-path-parameter }

            }

            if (plugins.size() != normalized.size()) {
                build.setPlugins(new ArrayList<>(normalized.values()));
            }
        }

        /*
         * NOTE: This is primarily to keep backward-compat with Maven 2.x which did not validate that dependencies are
         * unique within a single POM. Upon multiple declarations, 2.x just kept the last one but retained the order of

* <abbr title="Eine Methode des Machine Learning, die künstliche neuronale Netze mit zahlreichen versteckten Schichten zwischen Eingabe- und Ausgabeschicht verwendet und so eine umfassende interne Struktur entwickelt">Deep Learning</abbr>

### Das abbr gibt eine vollständige Phrase und eine Erklärung { #the-abbr-gives-a-full-phrase-and-an-explanation }

   * form does not contain the cached value, which will be recalculated when {@code get()} is called
   * on the deserialized instance.
   *
   * <p>When the underlying delegate throws an exception then this memoizing supplier will keep
   * delegating calls until it returns valid data.
   *
   * <p>If {@code delegate} is an instance created by an earlier call to {@code memoize}, it is
   * returned directly.
   */

# Sub-dependencies { #sub-dependencies }

You can create dependencies that have **sub-dependencies**.

They can be as **deep** as you need them to be.

**FastAPI** will take care of solving them.

## First dependency "dependable" { #first-dependency-dependable }

You could create a first dependency ("dependable") like:

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[8:9] *}

	case len(privateKeys) != 0:
		outputFileName := strings.TrimSuffix(outputFileName, ".zip")
		err = extractInspectV2(privateKeys, input, outputFileName)
	}
	if err != nil {

		var keep keepFileErr
		if !errors.As(err, &keep) {
			os.Remove(outputFileName)
		}
		fatalErr(err)
	}

	// Export xl.meta to stdout
	if *export {
		fatalErr(inspectToExportType(outputFileName, *djson))

Then set Cookies in it, and then return it:

{* ../../docs_src/response_cookies/tutorial001_py39.py hl[10:12] *}

/// tip

Keep in mind that if you return a response directly instead of using the `Response` parameter, FastAPI will return it directly.