not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.
    *   Wraps losses passed to the `compile` API (strings and v1 losses) which
        are not instances of v2 `Loss` class in `LossWrapper` class. => All
        losses will now use `SUM_OVER_BATCH_SIZE` reduction as default.

*  special, incidental, or consequential damages of any character      *
*  including, without limitation, damages for lost profits, loss of    *
*  goodwill, work stoppage, computer failure or malfunction, or any    *
*  and all other commercial damages or losses, even if such party      *
*  shall have been informed of the possibility of such damages. This   *

indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may...

# Klassen als Abhängigkeiten { #classes-as-dependencies }

Bevor wir tiefer in das **Dependency Injection** System eintauchen, lassen Sie uns das vorherige Beispiel verbessern.

## Ein `dict` aus dem vorherigen Beispiel { #a-dict-from-the-previous-example }

# Classes as Dependencies { #classes-as-dependencies }

Before diving deeper into the **Dependency Injection** system, let's upgrade the previous example.

## A `dict` from the previous example { #a-dict-from-the-previous-example }

In the previous example, we were returning a `dict` from our dependency ("dependable"):

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

# Classes como Dependências { #classes-as-dependencies }

Antes de nos aprofundarmos no sistema de **Injeção de Dependência**, vamos melhorar o exemplo anterior.

## `dict` do exemplo anterior { #a-dict-from-the-previous-example }

No exemplo anterior, nós retornávamos um `dict` da nossa dependência ("injetável"):

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

# Классы как зависимости { #classes-as-dependencies }

Прежде чем углубиться в систему **Внедрения Зависимостей**, давайте обновим предыдущий пример.

## `dict` из предыдущего примера { #a-dict-from-the-previous-example }

В предыдущем примере мы возвращали `dict` из нашей зависимости:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

Но затем мы получаем `dict` в параметре `commons` *функции-обработчика пути*.

# Clases como dependencias { #classes-as-dependencies }

Antes de profundizar en el sistema de **Inyección de Dependencias**, vamos a mejorar el ejemplo anterior.

## Un `dict` del ejemplo anterior { #a-dict-from-the-previous-example }

En el ejemplo anterior, estábamos devolviendo un `dict` de nuestra dependencia ("dependable"):

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

## Dependências parametrizadas { #parameterized-dependencies }

Todas as dependências que vimos até agora são funções ou classes fixas.

Mas podem ocorrer casos onde você deseja ser capaz de definir parâmetros na dependência, sem ter a necessidade de declarar diversas funções ou classes.

Vamos imaginar que queremos ter uma dependência que verifica se o parâmetro de consulta `q` possui um valor fixo.

 * modify it under the terms of the GNU Lesser General Public
 * License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 *
 * This library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *