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docs/ja/docs/tutorial/dependencies/dependencies-with-yield.md
opt raise dep -->> handler: Raise HTTPException handler -->> client: HTTP error response dep -->> dep: Raise other exception end dep ->> operation: Run dependency, e.g. DB session opt raise operation -->> handler: Raise HTTPException handler -->> client: HTTP error response operation -->> dep: Raise other exception end
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common-protos/k8s.io/api/authorization/v1/generated.proto
// Reason is optional. It indicates why a request was allowed or denied. // +optional optional string reason = 2; // EvaluationError is an indication that some error occurred during the authorization check. // It is entirely possible to get an error and be able to continue determine authorization status in spite of it. // For instance, RBAC can be missing a role, but enough roles are still present and bound to reason about the request.
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docs/de/docs/tutorial/query-params.md
```JSON { "detail": [ { "type": "missing", "loc": [ "query", "needy" ], "msg": "Field required", "input": null, "url": "https://errors.pydantic.dev/2.1/v/missing" } ] } ``` Da `needy` ein erforderlicher Parameter ist, müssen Sie ihn in der URL setzen: ``` http://127.0.0.1:8000/items/foo-item?needy=sooooneedy ```
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architecture/standards/0002-avoid-using-java-serialization.md
Java serialization is inherently Java-centric and does not support cross-language scenarios well. - **Type Safety:** Java serialization does not enforce type safety as strictly as some alternatives, potentially leading to runtime errors. ## Decision We do not use Java serialization. Instead, we use custom serialization where we explicitly describe how data objects should be serialized and deserialized.
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tensorflow/c/experimental/gradients/BUILD
"//tensorflow/c/experimental/ops:math_ops", "//tensorflow/c/experimental/ops:nn_ops", "//tensorflow/core/lib/llvm_rtti", "//tensorflow/core/platform:errors", "@com_google_absl//absl/types:span", ], ) cc_library( name = "not_differentiable", srcs = ["not_differentiable.cc"], hdrs = [ "not_differentiable.h", ],
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common-protos/k8s.io/api/imagepolicy/v1alpha1/generated.proto
optional bool allowed = 1; // Reason should be empty unless Allowed is false in which case it // may contain a short description of what is wrong. Kubernetes // may truncate excessively long errors when displaying to the user. // +optional optional string reason = 2; // AuditAnnotations will be added to the attributes object of the // admission controller request using 'AddAnnotation'. The keys should
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docs/de/docs/how-to/custom-request-and-route.md
!!! tip "Tipp" Um dasselbe Problem zu lösen, ist es wahrscheinlich viel einfacher, den `body` in einem benutzerdefinierten Handler für `RequestValidationError` zu verwenden ([Fehlerbehandlung](../tutorial/handling-errors.md#den-requestvalidationerror-body-verwenden){.internal-link target=_blank}). Dieses Beispiel ist jedoch immer noch gültig und zeigt, wie mit den internen Komponenten interagiert wird.
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docs/en/docs/tutorial/dependencies/index.md
And then it just returns a `dict` containing those values. !!! info FastAPI added support for `Annotated` (and started recommending it) in version 0.95.0. If you have an older version, you would get errors when trying to use `Annotated`. Make sure you [Upgrade the FastAPI version](../../deployment/versions.md#upgrading-the-fastapi-versions){.internal-link target=_blank} to at least 0.95.1 before using `Annotated`.
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analysis/analysis-api-fir/src/org/jetbrains/kotlin/analysis/api/fir/components/KtFirSymbolInfoProvider.kt
import org.jetbrains.kotlin.analysis.api.symbols.KtReceiverParameterSymbol import org.jetbrains.kotlin.analysis.api.symbols.KtSymbol import org.jetbrains.kotlin.analysis.utils.errors.requireIsInstance import org.jetbrains.kotlin.descriptors.ClassKind import org.jetbrains.kotlin.descriptors.annotations.AnnotationUseSiteTarget import org.jetbrains.kotlin.descriptors.annotations.KotlinTarget
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tensorflow/c/BUILD
"tf_buffer.h", ], copts = tf_copts(), visibility = ["//visibility:public"], deps = [ ":c_api_macros_hdrs", ":tf_buffer_internal", "//tensorflow/core/platform:errors", "//tensorflow/core/platform:platform_port", "//tensorflow/core/platform:protobuf", "//tensorflow/core/platform:status", ], ) tf_cuda_library( name = "tf_buffer_internal",
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