#### Pydantic-модель из содержимого другой модели

Как в примере выше мы получили `user_dict` из `user_in.dict()`, этот код:

```Python
user_dict = user_in.dict()
UserInDB(**user_dict)
```

будет равнозначен такому:

```Python
UserInDB(**user_in.dict())
```

...потому что `user_in.dict()` - это `dict`, и затем мы указываем, чтобы Python его "распаковал", когда передаём его в `UserInDB` и ставим перед ним `**`.

👆 💪 ⚙️ `jsonable_encoder` 👈.

⚫️ 📨 🎚, 💖 Pydantic 🏷, & 📨 🎻 🔗 ⏬:

//// tab | 🐍 3️⃣.6️⃣ & 🔛

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

////

//// tab | 🐍 3️⃣.1️⃣0️⃣ & 🔛

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

////

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

		{name: "dir2/d2file5", size: 300},
		{name: "dir1/dira/dafile", size: 100000},
		{name: "dir1/dira/dbfile", size: 200000},
		{name: "dir1/dira/dirasub/dcfile", size: 1000000},
		{name: "dir1/dira/dirasub/sublevel3/dccccfile", size: 10},
		{name: "dir1/dira/dirasub/sublevel3/dccccfile20", size: 20},
		{name: "dir1/dira/dirasub/sublevel3/dccccfile30", size: 30},
		{name: "dir1/dira/dirasub/sublevel3/dccccfile40", size: 40},
	}

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.dict.kuromoji;

import java.util.Date;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.fess.dict.DictionaryCreator;
import org.codelibs.fess.dict.DictionaryFile;
import org.codelibs.fess.dict.DictionaryItem;

import jakarta.annotation.PostConstruct;

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.dict.mapping;

import java.util.Date;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.fess.dict.DictionaryCreator;
import org.codelibs.fess.dict.DictionaryFile;
import org.codelibs.fess.dict.DictionaryItem;

import jakarta.annotation.PostConstruct;

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.dict.stemmeroverride;

import java.util.Date;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.fess.dict.DictionaryCreator;
import org.codelibs.fess.dict.DictionaryFile;
import org.codelibs.fess.dict.DictionaryItem;

import jakarta.annotation.PostConstruct;

import os
from typing import Any, Dict

from pdm.backend.hooks import Context

TIANGOLO_BUILD_PACKAGE = os.getenv("TIANGOLO_BUILD_PACKAGE", "fastapi")


def pdm_build_initialize(context: Context) -> None:
    metadata = context.config.metadata
    # Get custom config for the current package, from the env var
    config: Dict[str, Any] = context.config.data["tool"]["tiangolo"][
        "_internal-slim-build"

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

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

#### `Dict`

Чтобы определить `dict`, вы передаёте 2 параметра типов, разделённых запятыми.

Первый параметр типа предназначен для ключей `dict`.

Второй параметр типа предназначен для значений `dict`:

```Python hl_lines="1  4"
{!../../docs_src/python_types/tutorial008.py!}
```

Это означает:

* Переменная `prices` является `dict`:

# Codificador Compatível com JSON

Existem alguns casos em que você pode precisar converter um tipo de dados (como um modelo Pydantic) para algo compatível com JSON (como um `dict`, `list`, etc).

Por exemplo, se você precisar armazená-lo em um banco de dados.

Para isso, **FastAPI** fornece uma função `jsonable_encoder()`.

## Usando a função `jsonable_encoder`