# Detrás de un Proxy { #behind-a-proxy }

En muchas situaciones, usarías un **proxy** como Traefik o Nginx delante de tu app de FastAPI.

Estos proxies podrían manejar certificados HTTPS y otras cosas.

## Headers reenviados por el Proxy { #proxy-forwarded-headers }

  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v1.
_tf_uses_legacy_keras = (
    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
setattr(_current_module, "keras", _KerasLazyLoader(globals(), mode="v1"))
_module_dir = _module_util.get_parent_dir_for_name("keras._tf_keras.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__
if _tf_uses_legacy_keras:

Si integras tu API con un proveedor OAuth2, podrás autenticarte y regresar a la documentación de la API con las credenciales adquiridas. E interactuar con ella usando la autenticación real de OAuth2.

Swagger UI lo manejará detrás de escena para ti, pero necesita este auxiliar de "redirección".

///

### Crea una *path operation* para probarlo { #create-a-path-operation-to-test-it }

*   Keras 3.0 will be the default Keras version. You may need to update your
    script to use Keras 3.0.

*   Please refer to the new Keras documentation for Keras 3.0
    (https://keras.io/keras_3).

*   To continue using Keras 2.0, do the following.

*   1.  Install tf-keras via pip install tf-keras~=2.16

    1.  To switch tf.keras to use Keras 2 (tf-keras), set the environment

Verás el response JSON como:

```JSON
{"message": "Hello World"}
```

### Documentación interactiva de la API { #interactive-api-docs }

Ahora ve a <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

  "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.
# This aims to maintain TF compatibility with NumPy 1.x until 2025 b/361369076.

  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v2/3.
_tf_uses_legacy_keras = (
    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
setattr(_current_module, "keras", _KerasLazyLoader(globals()))
_module_dir = _module_util.get_parent_dir_for_name("keras._tf_keras.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__
if _tf_uses_legacy_keras:

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   jax
    #   keras-nightly
namex==0.0.9 \
    --hash=sha256:7bd4e4a2cc3876592111609fdf4cbe6ff19883adbe6b3b40d842fd340f77025e \
    --hash=sha256:8adfea9da5cea5be8f4e632349b4669e30172c7859e1fd97459fdf3b17469253
    # via keras-nightly
numpy==1.26.4 \
    --hash=sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   jax
    #   keras-nightly
namex==0.0.9 \
    --hash=sha256:7bd4e4a2cc3876592111609fdf4cbe6ff19883adbe6b3b40d842fd340f77025e \
    --hash=sha256:8adfea9da5cea5be8f4e632349b4669e30172c7859e1fd97459fdf3b17469253
    # via keras-nightly
numpy==1.26.4 \
    --hash=sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   jax
    #   keras-nightly
namex==0.0.9 \
    --hash=sha256:7bd4e4a2cc3876592111609fdf4cbe6ff19883adbe6b3b40d842fd340f77025e \
    --hash=sha256:8adfea9da5cea5be8f4e632349b4669e30172c7859e1fd97459fdf3b17469253
    # via keras-nightly
numpy==2.1.3 \
    --hash=sha256:016d0f6f5e77b0f0d45d77387ffa4bb89816b57c835580c3ce8e099ef830befe \