Porque podemos confiar en ella para recibir el `username` y `password`, ya que la controlamos.

Pero si estás construyendo una aplicación OAuth2 a la que otros se conectarían (es decir, si estás construyendo un proveedor de autenticación equivalente a Facebook, Google, GitHub, etc.) deberías usar uno de los otros flujos.

El más común es el flujo implícito.

    assert response.json() == snapshot(
        {
            "detail": [
                {
                    "type": "extra_forbidden",
                    "loc": ["header", "tool"],
                    "msg": "Extra inputs are not permitted",
                    "input": "plumbus",
                }
            ]
        }
    )


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")

## 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:

{* ../../docs_src/cookie_params/tutorial001_an_py310.py hl[9] *}

/// note | Technical Details

	if err != nil {
		t.Fatal(err)
	}
	fis, err := td.Readdir(0)
	if err != nil {
		t.Fatal(err)
	}
	for _, fi := range fis {
		if !fi.IsDir() {
			continue
		}

		// TODO(gri) remove extra pkg directory eventually
		goldenFile := filepath.Join("testdata", "src", "pkg", fi.Name(), "golden.txt")
		w := NewWalker(nil, "testdata/src/pkg")
		pkg, err := w.import_(fi.Name())
		if err != nil {

Using the below example document:
<img width="85%" src="https://bradley.codes/static/img/jstream-levels.gif" alt="jstream"/>

we can choose to extract and act only the objects within the top-level array:
```go
f, _ := os.Open("input.json")
decoder := jstream.NewDecoder(f, 1) // extract JSON values at a depth level of 1
for mv := range decoder.Stream() {
  fmt.Printf("%v\n ", mv.Value)
}
```

output:
```

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses em Estruturas de Dados Aninhadas { #dataclasses-in-nested-data-structures }

Você também pode combinar `dataclasses` com outras anotações de tipo para criar estruturas de dados aninhadas.

Em alguns casos, você ainda pode ter que usar a versão do Pydantic das `dataclasses`. Por exemplo, se você tiver erros com a documentação da API gerada automaticamente.

 * the License.
 */

package com.google.common.base;

import com.google.common.annotations.GwtCompatible;

/**
 * Holder for extra methods of {@code Objects} only in web. Intended to be empty for regular
 * version.
 */
@GwtCompatible

            }
            // Make sure (whatever extra priority is set) that resolver file/apache is selected
            configProps.put(FILE_TRANSPORTER_PRIORITY_KEY, RESOLVER_MAX_PRIORITY);
            configProps.put(APACHE_HTTP_TRANSPORTER_PRIORITY_KEY, RESOLVER_MAX_PRIORITY);
        } else if (MAVEN_RESOLVER_TRANSPORT_WAGON.equals(transport)) {
            // Make sure (whatever extra priority is set) that wagon is selected

        NullPointerException.class, () -> multimap().putAll(k3(), newArrayList(v3(), null)));

    Collection<V> values = multimap().get(k3());
    if (values.size() == 0) {
      expectUnchanged();
      // Be extra thorough in case internal state was corrupted by the expected null.
      assertEquals(new ArrayList<>(), new ArrayList<>(values));
      assertEquals(size, multimap().size());
    } else {

                }
            ]
        }
    )


def test_cookie_param_model_extra(client: TestClient):
    with client as c:
        c.cookies.set("session_id", "123")
        c.cookies.set("extra", "track-me-here-too")
        response = c.get("/items/")
    assert response.status_code == 200
    assert response.json() == snapshot(
        {"session_id": "123", "fatebook_tracker": None, "googall_tracker": None}
    )