## Import `Header` { #import-header }

First import `Header`:

{* ../../docs_src/header_params/tutorial001_an_py310.py hl[3] *}

## Declare `Header` parameters { #declare-header-parameters }

Then declare the header parameters using the same structure as with `Path`, `Query` and `Cookie`.

You can define the default value as well as all the extra validation or annotation parameters:

```Python
q: Annotated[str | None, Query(min_length=3)] = None
```

So, when you need to declare a value as required while using `Query`, you can simply not declare a default value:

{* ../../docs_src/query_params_str_validations/tutorial006_an_py39.py hl[9] *}

### Required, can be `None` { #required-can-be-none }

## Importe `Header` { #import-header }

Primeiro importe `Header`:

{* ../../docs_src/header_params/tutorial001_an_py310.py hl[3] *}

## Declare parâmetros de `Header` { #declare-header-parameters }

Então declare os paramêtros de cabeçalho usando a mesma estrutura que em `Path`, `Query` e `Cookie`.

O primeiro valor é o valor padrão, você pode passar todas as validações adicionais ou parâmetros de anotação:

    val releasedVersions: Property<ReleasedVersionsDetails>

}

interface ModuleTargetRuntimes {

    /**
     * Declare that this Gradle module runs as part of worker process.
     */
    val usedInWorkers: Property<Boolean>

    /**
     * Declare that this Gradle module runs as part of the wrapper or as part of a client process.
     * Client processes include the Tooling API client or the CLI client.

/// 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[16] *}

## Import `Cookie` { #import-cookie }

First import `Cookie`:

{* ../../docs_src/cookie_params/tutorial001_an_py310.py hl[3] *}

## Declare `Cookie` parameters { #declare-cookie-parameters }

Then declare the cookie parameters using the same structure as with `Path` and `Query`.

You can define the default value as well as all the extra validation or annotation parameters:

You can declare multiple path parameters and query parameters at the same time, **FastAPI** knows which is which.

And you don't have to declare them in any specific order.

They will be detected by name:

{* ../../docs_src/query_params/tutorial004_py310.py hl[6,8] *}

## Required query parameters { #required-query-parameters }

        val distributionRuntimeOnly = bucket("${prefix}TestDistributionRuntimeOnly", "Declare the distribution that is required to run tests")
        val localRepository = bucket("${prefix}TestLocalRepository", "Declare a local repository required as input data for the tests (e.g. :tooling-api)")

You can declare a `response_model`, using the default status code `200` (or a custom one if you need), and then declare additional information for that same response in `responses`, directly in the OpenAPI schema.

**FastAPI** will keep the additional information from `responses`, and combine it with the JSON Schema from your model.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Header Parameters with a Pydantic Model { #header-parameters-with-a-pydantic-model }