=== "Python 3.10+"

    ```Python hl_lines="4  21"
    {!> ../../../docs_src/encoder/tutorial001_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="5  22"
    {!> ../../../docs_src/encoder/tutorial001.py!}
    ```

In this example, it would convert the Pydantic model to a `dict`, and the `datetime` to a `str`.

⚫️ 📨 🎚, 💖 Pydantic 🏷, & 📨 🎻 🔗 ⏬:

=== "🐍 3️⃣.6️⃣ & 🔛"

    ```Python hl_lines="5  22"
    {!> ../../../docs_src/encoder/tutorial001.py!}
    ```

=== "🐍 3️⃣.1️⃣0️⃣ & 🔛"

    ```Python hl_lines="4  21"
    {!> ../../../docs_src/encoder/tutorial001_py310.py!}
    ```

👉 🖼, ⚫️ 🔜 🗜 Pydantic 🏷 `dict`, & `datetime` `str`.

=== "Python 3.10+"

    ```Python hl_lines="4  21"
    {!> ../../../docs_src/encoder/tutorial001_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="5  22"
    {!> ../../../docs_src/encoder/tutorial001.py!}
    ```

Neste exemplo, ele converteria o modelo Pydantic em um `dict`, e o `datetime` em um `str`.

对此你可以使用`jsonable_encoder`。

它接收一个对象，比如Pydantic模型，并会返回一个JSON兼容的版本：

=== "Python 3.10+"

    ```Python hl_lines="4  21"
    {!> ../../../docs_src/encoder/tutorial001_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="5  22"
    {!> ../../../docs_src/encoder/tutorial001.py!}
    ```

在这个例子中，它将Pydantic模型转换为`dict`，并将`datetime`转换为`str`。

같은 방식으로 이 데이터베이스는 Pydantic 모델(속성이 있는 객체)을 받지 않고, `dict` 만을 받습니다.

이를 위해 `jsonable_encoder` 를 사용할 수 있습니다.

Pydantic 모델과 같은 객체를 받고 JSON 호환 가능한 버전으로 반환합니다:

```Python hl_lines="5  22"
{!../../../docs_src/encoder/tutorial001.py!}
```

이 예시는 Pydantic 모델을 `dict`로, `datetime` 형식을 `str`로 변환합니다.

		}
		if a, e := buffer.String(), encoder.expectedResult; e != a {
			t.Errorf("%s: unexpected result: %s, expected: %s", encoder.identifier, a, e)
		}
	}
	for _, encoder := range encoders {
		if encoder.callsNumber != 1 {
			t.Errorf("%s: unexpected number of encode() calls: %d", encoder.identifier, encoder.callsNumber)
		}
	}
}

func TestCachingObjectFieldAccessors(t *testing.T) {

}

func (d *decoder) Close() error {
	return d.reader.Close()
}

type encoder struct {
	writer  io.Writer
	encoder runtime.Encoder
	buf     *bytes.Buffer
}

// NewEncoder returns a new streaming encoder.
func NewEncoder(w io.Writer, e runtime.Encoder) Encoder {
	return &encoder{
		writer:  w,
		encoder: e,
		buf:     &bytes.Buffer{},
	}
}

	creater := &mockCreater{obj: &runtimetesting.MockCacheableObject{}}
	typer := &mockTyper{gvk: &gvk}

	encoders := []runtime.Encoder{
		NewSerializer(creater, typer),
		NewRawSerializer(creater, typer),
	}

	for _, encoder := range encoders {
		runtimetesting.CacheableObjectTest(t, encoder)
	}
}

type mockCreater struct {
	apiVersion string
	kind       string
	err        error

	}

	s := serializer.Serializer

	// Give callers the opportunity to wrap encoders and decoders.  For decoders, each returned decoder will
	// be passed to the recognizer so that multiple decoders are available.
	var encoder runtime.Encoder = s
	if opts.EncoderDecoratorFn != nil {
		encoder = opts.EncoderDecoratorFn(encoder)
	}
	decoders := []runtime.Decoder{
		// selected decoder as the primary
		s,

 *      The WriteContext keeps track (among other things) of the low level binary stream (actually, a Gradle serialization {@link org.gradle.internal.serialize.Encoder Encoder}) and the codec to be used.
 *  </p>
 *  <p>
 *      Object serialization is a graph-walking process, and as such, it has a recursive nature.
 *  </p>
 *  <p></p>