password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Un modelo Pydantic a partir del contenido de otro { #a-pydantic-model-from-the-contents-of-another }

Como en el ejemplo anterior obtuvimos `user_dict` de `user_in.model_dump()`, este código:

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

sería equivalente a:

Beispielsweise können Sie [„with“ verwenden, um eine Datei auszulesen](https://docs.python.org/3/tutorial/inputoutput.html#reading-and-writing-files):

```Python
with open("./somefile.txt") as f:
    contents = f.read()
    print(contents)
```

Im Hintergrund erstellt das `open("./somefile.txt")` ein Objekt, das als „Kontextmanager“ bezeichnet wird.

- `close()`: ファイルを閉じます。

これらはすべて `async` メソッドなので、`await` する必要があります。

例えば、`async` の *path operation 関数* 内では次のように内容を取得できます:

```Python
contents = await myfile.read()
```

通常の `def` の *path operation 関数* 内にいる場合は、`UploadFile.file` に直接アクセスできます。例えば:

```Python
contents = myfile.file.read()
```

/// note | `async` の技術詳細

`async` メソッドを使うと、**FastAPI** はファイルメソッドをスレッドプールで実行し、その完了を待機します。

///

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
 * either express or implied. See the License for the specific language
 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.suggest.index.contents.document;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Queue;
import java.util.concurrent.ConcurrentLinkedQueue;
import java.util.concurrent.atomic.AtomicBoolean;

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Bir Pydantic Modelinden Diğerinin İçeriğiyle Pydantic Model Oluşturmak { #a-pydantic-model-from-the-contents-of-another }

Yukarıdaki örnekte `user_dict`'i `user_in.model_dump()` ile elde ettiğimiz için, şu kod:

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

şuna eşdeğerdir:

UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### 用另一个模型的内容生成 Pydantic 模型 { #a-pydantic-model-from-the-contents-of-another }

上例中 ，从 `user_in.model_dump()` 中得到了 `user_dict`，下面的代码：

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

等效于：

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

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Pydantic-модель из содержимого другой { #a-pydantic-model-from-the-contents-of-another }

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

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

будет равнозначен такому:

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Um modelo Pydantic a partir do conteúdo de outro { #a-pydantic-model-from-the-contents-of-another }

Como no exemplo acima, obtivemos o `user_dict` a partir do `user_in.model_dump()`, este código:

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

seria equivalente a:

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Ein Pydantic-Modell aus dem Inhalt eines anderen { #a-pydantic-model-from-the-contents-of-another }

Da wir im obigen Beispiel `user_dict` von `user_in.model_dump()` bekommen haben, wäre dieser Code:

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

gleichwertig zu:

    assertThat(parse("http://user:password@[::1]/").password).isEqualTo("password")
    assertThat(parse("http://user:password@[::1]:8080/").host).isEqualTo("::1")

    // Permit the contents of IPv6 addresses to be percent-encoded...
    assertThat(parse("http://[%3A%3A%31]/").host).isEqualTo("::1")

    // Including the Square braces themselves! (This is what Chrome does.)