$ pip install jinja2

---> 100%
```

</div>

## Usando `Jinja2Templates` { #using-jinja2templates }

* Importe `Jinja2Templates`.
* Crie um objeto `templates` que você possa reutilizar posteriormente.
* Declare um parâmetro `Request` no *path operation* que retornará um template.

 * }
 *
 * <p>Graph algorithms that need additional capabilities (accessing both predecessors and
 * successors, iterating over the edges, etc.) should declare their input to be of a type that
 * provides those capabilities, such as {@link Graph}, {@link ValueGraph}, or {@link Network}.
 *
 * <h3>Additional documentation</h3>
 *

 * }
 *
 * <p>Graph algorithms that need additional capabilities (accessing both predecessors and
 * successors, iterating over the edges, etc.) should declare their input to be of a type that
 * provides those capabilities, such as {@link Graph}, {@link ValueGraph}, or {@link Network}.
 *
 * <h3>Additional documentation</h3>
 *

* on the fly: на лету
* scratch the surface: поверхностно ознакомиться
* tip: совет (or `подсказка` depending on context)
* Pydantic model: Pydantic-модель (`модель Pydantic` and `Pydantic модель` are also fine)
* declare: объявить
* have the next best performance, after: быть на следующем месте по производительности после
* timing attack: тайминговая атака (clarify `атака по времени` if needed)

# Response Status Code { #response-status-code }

The same way you can specify a response model, you can also declare the HTTP status code used for the response with the parameter `status_code` in any of the *path operations*:

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* etc.

{* ../../docs_src/response_status_code/tutorial001_py39.py hl[6] *}

/// note

Se você está se perguntando, "AlexNet", "ResNet" e "LeNet" são apenas nomes de <abbr title="Tecnicamente, arquiteturas de modelos de Deep Learning">modelos</abbr> de Aprendizado de Máquina.
///

### Declare um parâmetro de path { #declare-a-path-parameter }

Em seguida, crie um *parâmetro de path* com anotação de tipo usando a classe enum que você criou (`ModelName`):

{* ../../docs_src/path_params/tutorial005_py39.py hl[16] *}

 * }
 *
 * <p>Graph algorithms that need additional capabilities (accessing both predecessors and
 * successors, iterating over the edges, etc.) should declare their input to be of a type that
 * provides those capabilities, such as {@link Graph}, {@link ValueGraph}, or {@link Network}.
 *
 * <h3>Additional documentation</h3>
 *

   * @param reference any array of the desired type
   * @param length the length of the new array
   */
  /*
   * The new array contains nulls, even if the old array did not. If we wanted to be accurate, we
   * would declare a return type of `@Nullable T[]`. However, we've decided not to think too hard
   * about arrays for now, as they're a mess. (We previously discussed this in the review of
   * ObjectArrays, which is the main caller of this method.)

FROM $BASE_IMAGE AS devel
# See https://docs.docker.com/reference/dockerfile/#understand-how-arg-and-from-interact
# on why we cannot reference BASE_IMAGE again unless we declare it again.
################################################################################

# Install devtoolset build dependencies
COPY setup.sources.sh /setup.sources.sh
COPY setup.packages.sh /setup.packages.sh

  }

  @CollectionSize.Require(SEVERAL)
  @MapFeature.Require(SUPPORTS_REMOVE)
  /*
   * ListMultimap.asMap essentially returns a Map<K, List<V>>; we just can't declare it that way.
   * Thus, calls like asMap().values().remove(someList) are safe because they are comparing a list
   * to a collection of other lists.
   */
  @SuppressWarnings("CollectionUndefinedEquality")