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docs/ko/docs/tutorial/body-nested-models.md
```Python hl_lines="14" {!../../../docs_src/body_nested_models/tutorial001.py!} ``` 이는 `tags`를 항목 리스트로 만듭니다. 각 항목의 타입을 선언하지 않더라도요. ## 타입 매개변수가 있는 리스트 필드 하지만 파이썬은 내부의 타입이나 "타입 매개변수"를 선언할 수 있는 특정 방법이 있습니다: ### typing의 `List` 임포트 먼저, 파이썬 표준 `typing` 모듈에서 `List`를 임포트합니다: ```Python hl_lines="1" {!../../../docs_src/body_nested_models/tutorial002.py!} ``` ### 타입 매개변수로 `List` 선언
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docs/pt/docs/tutorial/body-nested-models.md
## Modelos aninhados Cada atributo de um modelo Pydantic tem um tipo. Mas esse tipo pode ser outro modelo Pydantic. Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos. Tudo isso, aninhado arbitrariamente. ### Defina um sub-modelo Por exemplo, nós podemos definir um modelo `Image`: ```Python hl_lines="9-11"
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docs/zh/docs/tutorial/body-nested-models.md
=== "Python 3.10+" ```Python hl_lines="12" {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!} ``` === "Python 3.9+" ```Python hl_lines="14" {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!} ``` === "Python 3.8+" ```Python hl_lines="14" {!> ../../../docs_src/body_nested_models/tutorial002.py!} ``` ## Set 类型 但是随后我们考虑了一下,意识到标签不应该重复,它们很大可能会是唯一的字符串。
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docs/em/docs/tutorial/body-nested-models.md
```Python hl_lines="14" {!> ../../../docs_src/body_nested_models/tutorial002.py!} ``` === "🐍 3️⃣.9️⃣ & 🔛" ```Python hl_lines="14" {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!} ``` === "🐍 3️⃣.1️⃣0️⃣ & 🔛" ```Python hl_lines="12" {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!} ``` ## ⚒ 🆎
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docs/ja/docs/tutorial/body-nested-models.md
```Python hl_lines="12" {!../../../docs_src/body_nested_models/tutorial001.py!} ``` これにより、各項目の型は宣言されていませんが、`tags`はある項目のリストになります。 ## タイプパラメータを持つリストのフィールド しかし、Pythonには型や「タイプパラメータ」を使ってリストを宣言する方法があります: ### typingの`List`をインポート まず、Pythonの標準の`typing`モジュールから`List`をインポートします: ```Python hl_lines="1" {!../../../docs_src/body_nested_models/tutorial002.py!} ``` ### タイプパラメータを持つ`List`の宣言
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tests/test_duplicate_models_openapi.py
from fastapi import FastAPI from fastapi.testclient import TestClient from pydantic import BaseModel app = FastAPI() class Model(BaseModel): pass class Model2(BaseModel): a: Model class Model3(BaseModel): c: Model d: Model2 @app.get("/", response_model=Model3) def f(): return {"c": {}, "d": {"a": {}}} client = TestClient(app) def test_get_api_route():
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Fri Jun 30 18:25:16 GMT 2023 - 2.1K bytes - Viewed (0) -
SECURITY.md
TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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docs_src/sql_databases/sql_app_py310/crud.py
from sqlalchemy.orm import Session from . import models, schemas def get_user(db: Session, user_id: int): return db.query(models.User).filter(models.User.id == user_id).first() def get_user_by_email(db: Session, email: str): return db.query(models.User).filter(models.User.email == email).first() def get_users(db: Session, skip: int = 0, limit: int = 100): return db.query(models.User).offset(skip).limit(limit).all()
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Fri Jan 07 14:11:31 GMT 2022 - 1K bytes - Viewed (0) -
docs_src/sql_databases/sql_app/crud.py
from sqlalchemy.orm import Session from . import models, schemas def get_user(db: Session, user_id: int): return db.query(models.User).filter(models.User.id == user_id).first() def get_user_by_email(db: Session, email: str): return db.query(models.User).filter(models.User.email == email).first() def get_users(db: Session, skip: int = 0, limit: int = 100): return db.query(models.User).offset(skip).limit(limit).all()
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Thu Mar 26 19:09:53 GMT 2020 - 1K bytes - Viewed (0) -
docs_src/events/tutorial003.py
from fastapi import FastAPI def fake_answer_to_everything_ml_model(x: float): return x * 42 ml_models = {} @asynccontextmanager async def lifespan(app: FastAPI): # Load the ML model ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model yield # Clean up the ML models and release the resources ml_models.clear() app = FastAPI(lifespan=lifespan)
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