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

#### Unpacking a `dict` and extra keywords { #unpacking-a-dict-and-extra-keywords }

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

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

...ends up being like:

Así, obtenemos un modelo Pydantic a partir de los datos en otro modelo Pydantic.

#### Desempaquetando un `dict` y palabras clave adicionales { #unpacking-a-dict-and-extra-keywords }

Y luego agregando el argumento de palabra clave adicional `hashed_password=hashed_password`, como en:

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

...termina siendo como:

Auf diese Weise erhalten wir ein Pydantic-Modell aus den Daten eines anderen Pydantic-Modells.

#### Ein `dict` entpacken und zusätzliche Schlüsselwort-Argumente { #unpacking-a-dict-and-extra-keywords }

Und dann fügen wir das zusätzliche Schlüsselwort-Argument `hashed_password=hashed_password` hinzu, wie in:

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

Таким образом мы получаем Pydantic-модель на основе данных из другой Pydantic-модели.

#### Распаковка `dict` и дополнительные именованные аргументы { #unpacking-a-dict-and-extra-keywords }

И затем, если мы добавим дополнительный именованный аргумент `hashed_password=hashed_password` как здесь:

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

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

#### Desembrulhando um `dict` e palavras-chave extras { #unpacking-a-dict-and-extra-keywords }

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:

	Xattrs map[string]string

	// PAXRecords is a map of PAX extended header records.
	//
	// User-defined records should have keys of the following form:
	//	VENDOR.keyword
	// Where VENDOR is some namespace in all uppercase, and keyword may
	// not contain the '=' character (e.g., "GOLANG.pkg.version").
	// The key and value should be non-empty UTF-8 strings.
	//

</pre>
<pre>
a
_x9
ThisVariableIsExported
αβ
</pre>

<p>
Some identifiers are <a href="#Predeclared_identifiers">predeclared</a>.
</p>


<h3 id="Keywords">Keywords</h3>

<p>
The following keywords are reserved and may not be used as identifiers.
</p>
<pre class="grammar">
break        default      func         interface    select
case         defer        go           map          struct

                                    "schema": {
                                        "title": "Response Read Keyword Weights Keyword Weights  Get",
                                        "type": "object",
                                        "additionalProperties": {"type": "number"},
                                    }
                                }
                            },
                        }
                    },

and with different keywords. In particular, we now match NumPy order as
`tf.split(value, num_or_size_splits, axis)`. * `tf.sparse_split` now takes
arguments in reversed order and with different keywords. In particular we now
match NumPy order as `tf.sparse_split(sp_input, num_split, axis)`. NOTE: we have
temporarily made `tf.sparse_split` require keyword arguments. * `tf.concat` now

- request (HTTP): запит
- response (HTTP): відповідь
- path operation: операція шляху
- path operation function: функція операції шляху

### `///` admonitions

1) Keep the admonition keyword in English (do not translate `note`, `tip`, etc.).
2) If a title is present, prefer these canonical titles (choose one canonical form where variants exist):

- `/// note | Примітка`
- `/// note | Технічні деталі`