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fastapi/utils.py
return operation_id def deep_dict_update(main_dict: Dict[Any, Any], update_dict: Dict[Any, Any]) -> None: for key, value in update_dict.items(): if ( key in main_dict and isinstance(main_dict[key], dict) and isinstance(value, dict) ): deep_dict_update(main_dict[key], value) elif ( key in main_dict
Python - Registered: Sun May 05 07:19:11 GMT 2024 - Last Modified: Thu Apr 18 19:40:57 GMT 2024 - 7.8K bytes - Viewed (0) -
docs/en/docs/tutorial/path-params.md
!!! tip If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>. ### Declare a *path parameter* Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`): ```Python hl_lines="16"
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tests/test_tutorial/test_path_params/test_tutorial005.py
client = TestClient(app) def test_get_enums_alexnet(): response = client.get("/models/alexnet") assert response.status_code == 200 assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"} def test_get_enums_lenet(): response = client.get("/models/lenet") assert response.status_code == 200 assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Thu Sep 28 04:14:40 GMT 2023 - 5K bytes - Viewed (0) -
docs/en/layouts/custom.yml
Others - Registered: Sun May 05 07:19:11 GMT 2024 - Last Modified: Mon Jun 26 14:05:43 GMT 2023 - 6.5K bytes - Viewed (0) -
docs/de/docs/tutorial/path-params.md
!!! tip "Tipp" Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <abbr title="Genau genommen, Deep-Learning-Modellarchitekturen">Modellen</abbr> für maschinelles Lernen. ### Deklarieren Sie einen *Pfad-Parameter* Dann erstellen Sie einen *Pfad-Parameter*, der als Typ die gerade erstellte Enum-Klasse hat (`ModelName`):
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docs/ru/docs/tutorial/path-params.md
```Python hl_lines="18 21 23" {!../../../docs_src/path_params/tutorial005.py!} ``` Вы отправите клиенту такой JSON-ответ: ```JSON { "model_name": "alexnet", "message": "Deep Learning FTW!" } ``` ## Path-параметры, содержащие пути Предположим, что есть *операция пути* с путем `/files/{file_path}`.
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docs/zh/docs/tutorial/path-params.md
返回给客户端之前,要把枚举元素转换为对应的值(本例中为字符串): ```Python hl_lines="18 21 23" {!../../../docs_src/path_params/tutorial005.py!} ``` 客户端中的 JSON 响应如下: ```JSON { "model_name": "alexnet", "message": "Deep Learning FTW!" } ``` ## 包含路径的路径参数 假设*路径操作*的路径为 `/files/{file_path}`。 但需要 `file_path` 中也包含*路径*,比如,`home/johndoe/myfile.txt`。 此时,该文件的 URL 是这样的:`/files/home/johndoe/myfile.txt`。
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docs/fr/docs/tutorial/path-params.md
```Python hl_lines="18 21 23" {!../../../docs_src/path_params/tutorial005.py!} ``` Le client recevra une réponse JSON comme celle-ci : ```JSON { "model_name": "alexnet", "message": "Deep Learning FTW!" } ``` ## Paramètres de chemin contenant des chemins Disons que vous avez une *fonction de chemin* liée au chemin `/files/{file_path}`.
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docs_src/path_params/tutorial005.py
lenet = "lenet" app = FastAPI() @app.get("/models/{model_name}") async def get_model(model_name: ModelName): if model_name is ModelName.alexnet: return {"model_name": model_name, "message": "Deep Learning FTW!"} if model_name.value == "lenet": return {"model_name": model_name, "message": "LeCNN all the images"}
Python - Registered: Sun May 05 07:19:11 GMT 2024 - Last Modified: Fri Aug 26 13:26:03 GMT 2022 - 546 bytes - Viewed (0) -
docs/pt/docs/async.md
* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.
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