* the whole resource itself.
 *
 * @param <IN> a service specific type, representing a query of the service, which can be replayed later
 * @param <OUT> the fingerprint result of a service query, suitable for checking up-to-date status
 * @param <SERVICE> the type of the service
 */
public interface ImplicitInputsProvidingService<IN, OUT, SERVICE> {

    SERVICE withImplicitInputRecorder(ImplicitInputRecorder registrar);

// bound to `func`, which is bound to the TF_SavedModel it was loaded from.
TF_CAPI_EXPORT extern TF_FunctionMetadata* TF_ConcreteFunctionGetMetadata(
    TF_ConcreteFunction* func);

// Returns a TFE_Op suitable for executing this function. Caller must provide
// all function inputs in `inputs`, and must not add any additional inputs on
// the returned op. (i.e. don't call TFE_OpAddInput or TFE_OpAddInputList).

import org.gradle.internal.typeconversion.NotationParser;

import javax.annotation.Nullable;

/**
 * Adapts a mutable module component resolve metadata instance into a form that is suitable
 * for mutation through the Gradle DSL: we don't want to expose all the resolve component
 * metadata methods, only those which make sense, and that we can reason about safely. The adapter

type xchacha20poly1305 struct {
	key [KeySize]byte
}

// NewX returns a XChaCha20-Poly1305 AEAD that uses the given 256-bit key.
//
// XChaCha20-Poly1305 is a ChaCha20-Poly1305 variant that takes a longer nonce,
// suitable to be generated randomly without risk of collisions. It should be
// preferred when nonce uniqueness cannot be trivially ensured, or whenever
// nonces are randomly generated.
func NewX(key []byte) (cipher.AEAD, error) {

#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v2 {

// Run all the passes involved in transforming the graph before execution so
// that it is suitable for targeting devices when called with the TF 2 Function
// API. Users that need clustering with the Session API should use the v1 Bridge
// API. These transformations take as input a Tensorflow Graph as an MLIR Module

	ret := &unstructured.Unstructured{}
	ret.SetGroupVersionKind(kind)
	return ret, nil
}

type unstructuredDefaulter struct {
}

// NewUnstructuredDefaulter returns defaulter suitable for unstructured types that doesn't default anything
func NewUnstructuredDefaulter() runtime.ObjectDefaulter {
	return unstructuredDefaulter{}
}

func (d unstructuredDefaulter) Default(in runtime.Object) {

// a certain combination:
// - no object exists with suitable topology and storage class name
// - such an object exists, but the capacity is unset
// - such an object exists, but the capacity is zero
//
// The producer of these objects can decide which approach is more suitable.
//
// They are consumed by the kube-scheduler when a CSI driver opts into

	// See https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
	return uint32((uint64(uint32(rand())) * uint64(n)) >> 32)
}

// cheaprand is a non-cryptographic-quality 32-bit random generator
// suitable for calling at very high frequency (such as during scheduling decisions)
// and at sensitive moments in the runtime (such as during stack unwinding).
// it is "cheap" in the sense of both expense and quality.
//

    /**
     * Invoked when the code starts an external process. The command string with all argument is provided for reporting but its value may not be suitable to actually invoke the command because all
     * arguments are joined together (separated by space) and there is no escaping of special characters.
     *
     * @param command the command used to start the process (with arguments)

func NewRequest(method, target string, body io.Reader) *http.Request {
	return NewRequestWithContext(context.Background(), method, target, body)
}

// NewRequestWithContext returns a new incoming server Request, suitable
// for passing to an [http.Handler] for testing.
//
// The target is the RFC 7230 "request-target": it may be either a
// path or an absolute URL. If target is an absolute URL, the host name