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docs/en/docs/tutorial/response-status-code.md
/// The `status_code` parameter receives a number with the HTTP status code. /// info `status_code` can alternatively also receive an `IntEnum`, such as Python's [`http.HTTPStatus`](https://docs.python.org/3/library/http.html#http.HTTPStatus). /// It will: * Return that status code in the response.
Created: Sun Apr 05 07:19:11 GMT 2026 - Last Modified: Thu Mar 05 18:13:19 GMT 2026 - 3.9K bytes - Click Count (0) -
docs/en/docs/contributing.md
``` </div> It will serve the documentation on `http://127.0.0.1:8008`. That way, you can edit the documentation/source files and see the changes live. /// tip Alternatively, you can perform the same steps that scripts does manually. Go into the language directory, for the main docs in English it's at `docs/en/`: ```console $ cd docs/en/ ```
Created: Sun Apr 05 07:19:11 GMT 2026 - Last Modified: Mon Mar 23 13:59:26 GMT 2026 - 10.6K bytes - Click Count (0) -
docs/en/docs/advanced/behind-a-proxy.md
```JSON { "message": "Hello World", "root_path": "/api/v1" } ``` ### Setting the `root_path` in the FastAPI app { #setting-the-root-path-in-the-fastapi-app } Alternatively, if you don't have a way to provide a command line option like `--root-path` or equivalent, you can set the `root_path` parameter when creating your FastAPI app: {* ../../docs_src/behind_a_proxy/tutorial002_py310.py hl[3] *}Created: Sun Apr 05 07:19:11 GMT 2026 - Last Modified: Thu Mar 05 18:13:19 GMT 2026 - 15.8K bytes - Click Count (0) -
docs/en/docs/deployment/docker.md
/// tip Traefik has integrations with Docker, Kubernetes, and others, so it's very easy to set up and configure HTTPS for your containers with it. /// Alternatively, HTTPS could be handled by a cloud provider as one of their services (while still running the application in a container). ## Running on Startup and Restarts { #running-on-startup-and-restarts }
Created: Sun Apr 05 07:19:11 GMT 2026 - Last Modified: Thu Mar 05 18:13:19 GMT 2026 - 28.3K bytes - Click Count (1) -
RELEASE.md
mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True) return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var + 1e-10))` Alternatively, you can override `convolution_op`: `python class StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs, kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],
Created: Tue Apr 07 12:39:13 GMT 2026 - Last Modified: Mon Mar 30 18:31:38 GMT 2026 - 746.5K bytes - Click Count (3)