```JSON
{"item_id":"foo"}
```

## Paramètres de chemin typés

Vous pouvez déclarer le type d'un paramètre de chemin dans la fonction, en utilisant les annotations de type Python :


```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

Ici, `item_id` est déclaré comme `int`.

!!! check "vérifier"

					ast.Walk(r, spec.Type)
				}
				r.declare(spec, i, r.topScope, kind, spec.Names...)
			}
		case token.TYPE:
			for _, spec := range n.Specs {
				spec := spec.(*ast.TypeSpec)
				// Go spec: The scope of a type identifier declared inside a function begins
				// at the identifier in the TypeSpec and ends at the end of the innermost
				// containing block.
				r.declare(spec, nil, r.topScope, ast.Typ, spec.Name)

 * way, a {@link Dependency} object is created. You can use this object to query or further configure the
 * dependency.</p>
 *
 * <p>You can also always add instances of
 * {@link org.gradle.api.artifacts.Dependency} directly:</p>
 *
 * <code><i>configurationName</i> &lt;instance&gt;</code>
 *

 *
 * The type of the enum entry is the enum class itself. The members declared in an enum entry's body are local to the body and cannot be
 * accessed from the outside. Hence, while it might look like enum entries can declare their own members (see the example below), they do
 * not have a (declared) member scope.
 *

[[sec:custom_versioning_scheme]]
=== Implementing a custom versioning scheme

In some corporate environments, the list of module versions that can be declared in Gradle builds is maintained and audited externally.
Dependency resolve rules provide a neat implementation of this pattern:

* In the build script, the developer declares dependencies with the module group and name, but uses a placeholder version, for example: `default`.

## `response_model` Parameter

There are some cases where you need or want to return some data that is not exactly what the type declares.

For example, you could want to **return a dictionary** or a database object, but **declare it as a Pydantic model**. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).

		check.error(ident, InvalidInitDecl, "cannot declare init - must be func")
		return
	}

	// spec: "The main package must have package name main and declare
	// a function main that takes no arguments and returns no value."
	if ident.Name == "main" && check.pkg.name == "main" {
		check.error(ident, InvalidMainDecl, "cannot declare main - must be func")
		return
	}

	check.declare(check.pkg.scope, ident, obj, nopos)

	sig.scope = check.scope
	defer check.closeScope()

	if recvPar != nil {
		// collect generic receiver type parameters, if any
		// - a receiver type parameter is like any other type parameter, except that it is declared implicitly
		// - the receiver specification acts as local declaration for its type parameters, which may be blank
		_, rname, rparams := check.unpackRecv(recvPar.Type, true)
		if len(rparams) > 0 {

If you don't declare the normalization strategy, outputs of the task cannot be reused between machines or between different locations on the same machine.
In short, without normalization, caching is highly ineffective.

To fix this problem, you need to declare a normalization strategy, by applying one of the following annotations:

	defer check.closeScope()

	if recvPar != nil && len(recvPar.List) > 0 {
		// collect generic receiver type parameters, if any
		// - a receiver type parameter is like any other type parameter, except that it is declared implicitly
		// - the receiver specification acts as local declaration for its type parameters, which may be blank
		_, rname, rparams := check.unpackRecv(recvPar.List[0].Type, true)
		if len(rparams) > 0 {