Sebastián Ramírez <******@****.***> 1728247014 +0200

Então, obtemos um modelo Pydantic a partir dos dados em outro modelo Pydantic.

#### Desembrulhando um `dict` e palavras-chave extras

E, então, adicionando o argumento de palavra-chave extra `hashed_password=hashed_password`, como em:

```Python
UserInDB(**user_in.dict(), hashed_password=hashed_password)
```

...acaba sendo como:

```Python
UserInDB(

So, we get a Pydantic model from the data in another Pydantic model.

#### Unwrapping a `dict` and extra keywords

And then adding the extra keyword argument `hashed_password=hashed_password`, like in:

```Python
UserInDB(**user_in.dict(), hashed_password=hashed_password)
```

...ends up being like:

```Python

Sebastián Ramírez <******@****.***> 1728247014 +0200

Sebastián Ramírez <******@****.***> 1728247014 +0200

# Extra Data Types

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

## Other data types

Sebastián Ramírez <******@****.***> 1728247014 +0200

Sebastián Ramírez <******@****.***> 1728247014 +0200

Sebastián Ramírez <******@****.***> 1728247014 +0200

Sebastián Ramírez <******@****.***> 1728247014 +0200