func extr1(x, x2 uint64) uint64 {
	return x<<7 + x2>>57 // arm64:"EXTR\t[$]57,"
}

func extr2(x, x2 uint64) uint64 {
	return x<<7 | x2>>57 // arm64:"EXTR\t[$]57,"
}

func extr3(x, x2 uint64) uint64 {
	return x<<7 ^ x2>>57 // arm64:"EXTR\t[$]57,"
}

func extr4(x, x2 uint32) uint32 {
	return x<<7 + x2>>25 // arm64:"EXTRW\t[$]25,"
}

func extr5(x, x2 uint32) uint32 {

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

# 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!}
    ```

apiVersion: release-notes/v2
kind: bug-fix
area: telemetry
issue:
- 31270
releaseNotes:
- |
  **Fixed** an issue where envoy does not start up properly when

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(

# 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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