==================

These tests use the [hpack-test-case][1] project to validate OkHttp's HPACK
implementation.  The HPACK test cases are in a separate git submodule, so to
initialize them, you must run:

    git submodule init
    git submodule update

TODO
----

 * Add maven goal to avoid manual call to git submodule init.
 * Make hpack-test-case update itself from git, and run new tests.

  )

# Add Keras module aliases
_losses = _KerasLazyLoader(globals(), submodule="losses", name="losses")
_metrics = _KerasLazyLoader(globals(), submodule="metrics", name="metrics")
_optimizers = _KerasLazyLoader(
    globals(), submodule="optimizers", name="optimizers")
_initializers = _KerasLazyLoader(
    globals(), submodule="initializers", name="initializers")
setattr(_current_module, "losses", _losses)

## Pydantic v1 in v2 { #pydantic-v1-in-v2 }

Pydantic v2 includes everything from Pydantic v1 as a submodule `pydantic.v1`. But this is no longer supported in versions above Python 3.13.

This means that you can install the latest version of Pydantic v2 and import and use the old Pydantic v1 components from this submodule, as if you had the old Pydantic v1 installed.

{* ../../docs_src/pydantic_v1_in_v2/tutorial001_an_py310.py hl[1,4] *}

[submodule "okhttp-hpacktests/src/test/resources/hpack-test-case"]
	path = okhttp-hpacktests/src/test/resources/hpack-test-case

  @ParameterizedTest
  @ArgumentsSource(StoriesTestProvider::class)
  fun testGoodDecoderInterop(story: Story) {
    assumeFalse(
      story === Story.MISSING,
      "Test stories missing, checkout git submodule",
    )
    testDecoder(story)
  }

  internal class StoriesTestProvider : SimpleProvider() {
    override fun arguments(): List<Any> = createStories(HpackJsonUtil.storiesForCurrentDraft())
  }

setattr(_current_module, "flags", flags)

_major_api_version = 1

# Add module aliases from Keras to TF.
# Some tf endpoints actually lives under Keras.
_current_module.layers = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.layers",
    name="layers",
    mode="v1")
if _tf_uses_legacy_keras:
  _module_dir = _module_util.get_parent_dir_for_name(
      "tf_keras.api._v1.keras.__internal__.legacy.layers")
else:

  @ParameterizedTest
  @ArgumentsSource(StoriesTestProvider::class)
  fun testRoundTrip(story: Story) {
    assumeFalse(
      story === Story.MISSING,
      "Test stories missing, checkout git submodule",
    )

    val newCases = mutableListOf<Case>()
    for (case in story.cases) {
      hpackWriter.writeHeaders(case.headersList)
      newCases += case.copy(wire = bytesOut.readByteString())
    }

* recap (noun): резюме
* utility function: вспомогательная функция
* fast to code: позволяет быстро писать код
* Tutorial - User Guide: Учебник - Руководство пользователя
* submodule: подмодуль
* subpackage: подпакет
* router: роутер
* building, deploying, accessing (when describing features of FastAPI Cloud): созданиe образа, развертывание и доступ
* type checker tool: инструмент проверки типов

Todo eso, de manera arbitraria.

### Definir un submodelo { #define-a-submodel }

Por ejemplo, podemos definir un modelo `Image`:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

### Usar el submodelo como tipo { #use-the-submodel-as-a-type }

Y luego podemos usarlo como el tipo de un atributo:

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

### Use the submodel as a type { #use-the-submodel-as-a-type }

And then we can use it as the type of an attribute: