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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
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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"}
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docs/en/layouts/custom.yml
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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_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/es/docs/tutorial/path-params.md
!!! tip "Consejo" Si lo estás dudando, "AlexNet", "ResNet", y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning. ### Declara un *parámetro de path* Luego, crea un *parámetro de path* con anotaciones de tipos usando la clase enum que creaste (`ModelName`): ```Python hl_lines="16"
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docs/pt/docs/tutorial/path-params.md
!!! tip "Dica" Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina). ### Declare um *parâmetro de rota* Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`):
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docs/em/docs/tutorial/path-params.md
!!! info <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">🔢 (⚖️ 🔢) 💪 🐍</a> ↩️ ⏬ 3️⃣.4️⃣. !!! tip 🚥 👆 💭, "📊", "🎓", & "🍏" 📛 🎰 🏫 <abbr title="Technically, Deep Learning model architectures">🏷</abbr>. ### 📣 *➡ 🔢* ⤴️ ✍ *➡ 🔢* ⏮️ 🆎 ✍ ⚙️ 🔢 🎓 👆 ✍ (`ModelName`): ```Python hl_lines="16" {!../../../docs_src/path_params/tutorial005.py!} ``` ### ✅ 🩺
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docs/en/docs/tutorial/dependencies/sub-dependencies.md
# Sub-dependencies You can create dependencies that have **sub-dependencies**. They can be as **deep** as you need them to be. **FastAPI** will take care of solving them. ## First dependency "dependable" You could create a first dependency ("dependable") like: === "Python 3.10+" ```Python hl_lines="8-9" {!> ../../../docs_src/dependencies/tutorial005_an_py310.py!} ``` === "Python 3.9+"
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