register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64_cuda")

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.

	<ul>
	<li>there are no "break" statements referring to the "for" statement, and</li>
	<li>the loop condition is absent, and</li>
	<li>the "for" statement does not use a range clause.</li>
	</ul>
</li>

<li>
	A <a href="#Switch_statements">"switch" statement</a> in which:
	<ul>
	<li>there are no "break" statements referring to the "switch" statement,</li>
	<li>there is a default case, and</li>

Create functions with a name that starts with `test_` (this is standard `pytest` conventions).

Use the `TestClient` object the same way as you do with `httpx`.

Write simple `assert` statements with the standard Python expressions that you need to check (again, standard `pytest`).

{* ../../docs_src/app_testing/tutorial001_py39.py hl[2,12,15:18] *}

/// tip

Only the code prior to and including the `yield` statement is executed before creating a response:

{* ../../docs_src/dependencies/tutorial007_py39.py hl[2:4] *}

The yielded value is what is injected into *path operations* and other dependencies:

{* ../../docs_src/dependencies/tutorial007_py39.py hl[4] *}

The code following the `yield` statement is executed after the response:

{* ../../docs_src/custom_response/tutorial008_py39.py hl[2,10:12,14] *}

1. This is the generator function. It's a "generator function" because it contains `yield` statements inside.
2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.

    define_values = {"no_xla_deps_in_cuda": "true"},
    visibility = ["//visibility:public"],
)

# Crosses between framework_shared_object and a bunch of other configurations
# due to limitations in nested select() statements.
config_setting(
    name = "framework_shared_object",
    define_values = {"framework_shared_object": "true"},
    visibility = ["//visibility:public"],
)

config_setting(

# Testing WebSockets { #testing-websockets }

You can use the same `TestClient` to test WebSockets.

For this, you use the `TestClient` in a `with` statement, connecting to the WebSocket:

{* ../../docs_src/app_testing/tutorial002_py39.py hl[27:31] *}

/// note

For more details, check Starlette's documentation for <a href="https://www.starlette.dev/testclient/#testing-websocket-sessions" class="external-link" target="_blank">testing WebSockets</a>.

* unload: quitar de memoria (do not translate to "descargar")
* mount (noun): mount (do not translate to "montura")
* mount (verb): montar
* statement (as in code statement): statement (do not translate to "declaración" or "sentencia")
* worker process: worker process (do not translate to "proceso trabajador" or "proceso de trabajo")

# Eventos de testing: lifespan y startup - shutdown { #testing-events-lifespan-and-startup-shutdown }

Cuando necesitas que `lifespan` se ejecute en tus tests, puedes usar el `TestClient` con un statement `with`:

{* ../../docs_src/app_testing/tutorial004_py39.py hl[9:15,18,27:28,30:32,41:43] *}


Puedes leer más detalles sobre ["Ejecutar lifespan en tests en el sitio oficial de documentación de Starlette."](https://www.starlette.dev/lifespan/#running-lifespan-in-tests)

# Probando WebSockets { #testing-websockets }

Puedes usar el mismo `TestClient` para probar WebSockets.

Para esto, usas el `TestClient` en un statement `with`, conectándote al WebSocket:

{* ../../docs_src/app_testing/tutorial002_py39.py hl[27:31] *}

/// note | Nota