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(

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		for {
			q := queueItem{
				input: next,
				dst:   make(chan [][]string, 1),
				err:   nextErr,
			}
			select {
			case <-r.close:
				return
			case r.queue <- &q:
			}

			select {
			case <-r.close:
				return
			case r.input <- &q:
			}
			if nextErr != nil {
				// Exit on any error.
				return
			}
			next, nextErr = r.nextSplit(csvSplitSize, r.bufferPool.Get().([]byte))

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# Declare Request Example Data

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

=== "Python 3.10+ Pydantic v2"

    ```Python hl_lines="13-24"
    {!> ../../../docs_src/schema_extra_example/tutorial001_py310.py!}
    ```

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Alejandra <******@****.***> 1711071731 -0500

# 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

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Alejandra <******@****.***> 1711071731 -0500