Bu fonksiyon, zaten tür belirteçlerine sahip:

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial003.py!}
```

Editör değişkenlerin tiplerini bildiğinden, yalnızca otomatik tamamlama değil, hata kontrolleri de sağlar:

<img src="/img/python-types/image04.png">

Artık `age` değişkenini `str(age)` olarak kullanmanız gerektiğini biliyorsunuz:

```Python hl_lines="2"

Eğer tarayıcınızda şu bağlantıyı:

```
http://127.0.0.1:8000/items/foo-item
```

...`needy` parametresini eklemeden açarsanız şuna benzer bir hata ile karşılaşırsınız:

```JSON
{
  "detail": [
    {
      "type": "missing",
      "loc": [
        "query",
        "needy"
      ],
      "msg": "Field required",
      "input": null,

Krishnan Parthasarathi <******@****.***> 1714735138 -0700

	}

	for i, testCase := range testCases {
		data, err := xml.Marshal(testCase.name)
		expectErr := (err != nil)

		if expectErr != testCase.expectErr {
			t.Fatalf("test %v: error: expected: %v, got: %v", i+1, testCase.expectErr, expectErr)
		}

		if !testCase.expectErr {
			if !reflect.DeepEqual(data, testCase.expectedData) {

def test_sub_duplicates():
    response = client.post("/with-duplicates-sub", json={"data": "myitem"})
    assert response.status_code == 200, response.text
    assert response.json() == [
        {"data": "myitem"},
        [{"data": "myitem"}, {"data": "myitem"}],
    ]


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

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info

    display: inline-block;
    vertical-align: middle;
}

[data-ty="input"]:before,
[data-ty-prompt]:before {
    margin-right: 0.75em;
    color: var(--color-text-subtle);
}

[data-ty="input"]:before {
    content: '$';
}

[data-ty][data-ty-prompt]:before {
    content: attr(data-ty-prompt);
}

[data-ty-cursor]:after {
    content: attr(data-ty-cursor);
    font-family: monospace;
    margin-left: 0.5em;

 * identity of a model. The tag allows for further classification of the associated data on the sole discretion of the
 * model builder.
 *
 */
public interface ModelCache {

    <T> T computeIfAbsent(String groupId, String artifactId, String version, String tag, Supplier<T> data);

    <T> T computeIfAbsent(Source path, String tag, Supplier<T> data);

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would...

    client = TestClient(app)
    return client


@needs_pydanticv2
def test_post(client: TestClient):
    yaml_data = """
        name: Deadpoolio
        tags:
        - x-force
        - x-men
        - x-avengers
        """
    response = client.post("/items/", content=yaml_data)
    assert response.status_code == 200, response.text
    assert response.json() == {
        "name": "Deadpoolio",