// * Prevent starvation of watch opened by cacher, by moving it to separate Watch RPC than watch request that bypass cacher.
		// * Ensure that progress notification requests are executed on the same Watch RPC as their watch, which is required for it to work.
		contextMetadata = metadata.New(map[string]string{"source": "cache"})
	}

func newPodNominator(podLister listersv1.PodLister) *nominator {
	return &nominator{
		podLister:          podLister,
		nominatedPods:      make(map[string][]*framework.PodInfo),
		nominatedPodToNode: make(map[types.UID]string),
	}
}

func podInfoKeyFunc(obj interface{}) (string, error) {
	return cache.MetaNamespaceKeyFunc(obj.(*framework.QueuedPodInfo).Pod)

			crc := expPart.Checksums[checksumType.String()]
			if crc == "" {
				return oi, InvalidPart{
					PartNumber: part.PartNumber,
				}
			}
			wantCS := map[string]string{
				hash.ChecksumCRC32.String():  part.ChecksumCRC32,
				hash.ChecksumCRC32C.String(): part.ChecksumCRC32C,
				hash.ChecksumSHA1.String():   part.ChecksumSHA1,
				hash.ChecksumSHA256.String(): part.ChecksumSHA256,
			}

	"istio.io/istio/security/pkg/util"
)

type caOptions struct {
	ExternalCAType   ra.CaExternalType
	ExternalCASigner string
	// domain to use in SPIFFE identity URLs
	TrustDomain      string
	Namespace        string
	Authenticators   []security.Authenticator
	CertSignerDomain string
}

// Based on istio_ca main - removing creation of Secrets with private keys in all namespaces and install complexity.
//

#     avx_win:                Build with avx instruction set on windows
#
# Other build options:
#     short_logs:       Only log errors during build, skip warnings.
#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).

internal
enum class StateType(val encryptable: Boolean = false) {
    /**
     * Contains the state for the entire build.
     */
    Work(true),

    /**
     * Contains the side effects observed during the creation of the [Model].
     */
    ModelSideEffects(true),

    /**
     * Contains the model objects sent back to the IDE in response to a TAPI request.
     */
    Model(true),

    /**

        protected final List<String> executedTasks;
        protected final Set<String> skippedTasks;
        private final ExecutionResult outputResult;

        InProcessExecutionResult(List<String> executedTasks, Set<String> skippedTasks, ExecutionResult outputResult) {
            this.executedTasks = executedTasks;
            this.skippedTasks = skippedTasks;

        }
    }

    void outputContains(String string) {
        assertHasResult()
        result.assertOutputContains(string.trim())
    }

    void postBuildOutputContains(String string) {
        assertHasResult()
        result.assertHasPostBuildOutput(string.trim())
    }

    void postBuildOutputDoesNotContain(String string) {
        assertHasResult()

nahme-eines-scikit-learn-modells-mit-onnx-und-fastapi/ title: Inbetriebnahme eines scikit-learn-Modells mit ONNX und FastAPI - author: Felix Schürmeyer author_link: https://hellocoding.de/autor/felix-schuermeyer/ link: https://hellocoding.de/blog/coding-language/python/fastapi title: REST-API Programmieren mittels Python und dem FastAPI Modul Japanese: - author: '@bee2' author_link: https://qiita.com/bee2 link: https://qiita.com/bee2/items/75d9c0d7ba20e7a4a0e9 title: '[FastAPI] Python製のASGI Web フレームワーク...

#ifndef TENSORFLOW_COMPILER_MLIR_TENSORFLOW_TRANSFORMS_PASSES_H_
#define TENSORFLOW_COMPILER_MLIR_TENSORFLOW_TRANSFORMS_PASSES_H_

#include <cstdint>
#include <memory>
#include <string>

#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/STLFunctionalExtras.h"
#include "llvm/Support/CommandLine.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project