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pyproject.toml
] [tool.mypy] strict = true [[tool.mypy.overrides]] module = "fastapi.concurrency" warn_unused_ignores = false ignore_missing_imports = true [[tool.mypy.overrides]] module = "fastapi.tests.*" ignore_missing_imports = true check_untyped_defs = true [[tool.mypy.overrides]] module = "docs_src.*" disallow_incomplete_defs = false disallow_untyped_defs = false
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docs/en/docs/tutorial/index.md
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doc/next/3-tools.md
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docs/en/docs/deployment/manually.md
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docs/en/docs/fastapi-cli.md
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docs/en/docs/advanced/settings.md
### Create the `Settings` object Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model. The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values. You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`. === "Pydantic v2"
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CONTRIBUTING.md
git config user.email ******@****.*** #### Import Gradle into IntelliJ To import Gradle into IntelliJ: - Open the `build.gradle.kts` file with IntelliJ and choose "Open as Project" - Make sure "Create separate module per source set" is selected - Make sure "Use default gradle wrapper" is selected - Select a Java 11 VM as "Gradle JVM" - In the "File already exists" dialogue, choose "Yes" to overwrite
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cmd/data-scanner.go
objectName: path.Base(entName), debug: f.dataUsageScannerDebug, lifeCycle: activeLifeCycle, replication: replicationCfg, } item.heal.enabled = thisHash.modAlt(f.oldCache.Info.NextCycle/folder.objectHealProbDiv, f.healObjectSelect/folder.objectHealProbDiv) && globalIsErasure item.heal.bitrot = f.scanMode == madmin.HealDeepScan // if the drive belongs to an erasure set
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docs/en/docs/release-notes.md
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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