* The file `app/routers/items.py` is inside a package, `app/routers/`, so, it's a submodule: `app.routers.items`.
* The same with `app/routers/users.py`, it's another submodule: `app.routers.users`.
* There's also a subdirectory `app/internal/` with another file `__init__.py`, so it's another "Python subpackage": `app.internal`.
* And the file `app/internal/admin.py` is another submodule: `app.internal.admin`.

  )

# 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)

* Das Gleiche gilt für `app/routers/users.py`, es ist ein weiteres Submodul: `app.routers.users`.
* Es gibt auch ein Unterverzeichnis `app/internal/` mit einer weiteren Datei `__init__.py`, es handelt sich also um ein weiteres „Python-Subpackage“: `app.internal`.
* Und die Datei `app/internal/admin.py` ist ein weiteres Submodul: `app.internal.admin`.

## 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] *}

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 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())
  }

  @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())
    }

import org.eclipse.aether.transfer.ArtifactNotFoundException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * Strategy for collecting Maven projects from the multi-module project root, even when executed in a submodule.
 */
@Named("MultiModuleCollectionStrategy")
@Singleton
public class MultiModuleCollectionStrategy implements ProjectCollectionStrategy {

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:

* Lo mismo con `app/routers/users.py`, es otro submódulo: `app.routers.users`.
* También hay un subdirectorio `app/internal/` con otro archivo `__init__.py`, por lo que es otro "subpaquete de Python": `app.internal`.
* Y el archivo `app/internal/admin.py` es otro submódulo: `app.internal.admin`.