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tests/test_tutorial/test_dataclasses/test_tutorial003.py
"description": "The kombucha mushroom people's favorite", }, {"name": "Pad Thai", "description": None}, { "name": "Lonely Night", "description": "The mostests lonliest nightiest of allest", }, ], }, ] @needs_pydanticv2 def test_openapi_schema():
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docs_src/dataclasses/tutorial003.py
"name": "Salt", "description": "The kombucha mushroom people's favorite", }, {"name": "Pad Thai"}, { "name": "Lonely Night", "description": "The mostests lonliest nightiest of allest", }, ], },
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docs/en/docs/tutorial/background-tasks.md
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docs/en/docs/async.md
--- If you just don't know, use normal `def`. --- **Note**: You can mix `def` and `async def` in your *path operation functions* as much as you need and define each one using the best option for you. FastAPI will do the right thing with them. Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast. But by following the steps above, it will be able to do some performance optimizations.
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docs/en/docs/deployment/docker.md
### Single Container If you have a simple setup, with a **single container** that then starts multiple **worker processes** (or also just one process), then you could run those previous steps in the same container, right before starting the process with the app. The official Docker image supports this internally.
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docs/ur/docs/benchmarks.md
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docs/zh-hant/docs/index.md
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docs/ko/docs/index.md
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docs/en/docs/tutorial/debugging.md
* Select "Python" * Run the debugger with the option "`Python: Current File (Integrated Terminal)`". It will then start the server with your **FastAPI** code, stop at your breakpoints, etc. Here's how it might look: <img src="/img/tutorial/debugging/image01.png"> --- If you use Pycharm, you can: * Open the "Run" menu. * Select the option "Debug...". * Then a context menu shows up.
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docs/en/docs/advanced/index.md
Or it might be the case that you just prefer to take other courses because they adapt better to your learning style.
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