│   ├── __init__.py      # este archivo hace que "app" sea un "paquete de Python"
│   ├── main.py          # módulo "main", por ejemplo import app.main
│   ├── dependencies.py  # módulo "dependencies", por ejemplo import app.dependencies
│   └── routers          # "routers" es un "subpaquete de Python"
│   │   ├── __init__.py  # hace que "routers" sea un "subpaquete de Python"

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

```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Değişkeni str olarak belirt
# ve o fonksiyon için harika bir editör desteği al
def main(user_id: str):
    return user_id


# Pydantic modeli
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sonrasında bu şekilde kullanabilirsin

```Python

Wystarczy, że napiszesz standardowe deklaracje typów Pythona:

```Python
from datetime import date

from pydantic import BaseModel

# Zadeklaruj parametr jako str
# i uzyskaj wsparcie edytora wewnątrz funkcji
def main(user_id: str):
    return user_id


# Model Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

A one będą mogły zostać później użyte w następujący sposób:

```Python

```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Deklarieren Sie eine Variable als ein `str`
# und bekommen Sie Editor-Unterstütung innerhalb der Funktion
def main(user_id: str):
    return user_id


# Ein Pydantic-Modell
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Das kann nun wie folgt verwendet werden:

```Python

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

That can then be used like:

```Python

enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world...

    assertThat(decoded).isEqualTo(date("2049-12-31T23:59:59.000+0000").time)
  }

  @Test fun `time before 1950 uses GENERALIZED_TIME`() {
    val generalizedTimeDer = "180f31393439313233313233353935395a".decodeHex()

    val decoded = CertificateAdapters.time.fromDer(generalizedTimeDer)
    val encoded = CertificateAdapters.time.toDer(decoded)

    assertThat(decoded).isEqualTo(date("1949-12-31T23:59:59.000+0000").time)

     *       operations in a thread always happen-before subsequent read operations in the same
     *       thread
     *   <li>If this field's value == null because of unsafe publication, we know that it isn't the
     *       object associated with our thread, because if it was the publication wouldn't have been

- The `EndpointSliceTerminatingCondition` feature gate was graduated to GA. The gate is now locked and will be removed in v1.28. ([#113351](https://github.com/kubernetes/kubernetes/pull/113351), [@andrewsykim](https://github.com/andrewsykim))
- `DynamicKubeletConfig` feature gate has been removed from the API server.
  Dynamic kubelet reconfiguration now can't be used even when older nodes are still