// marking as a fraction of GOMAXPROCS.
	//
	// Increasing the goal utilization will shorten GC cycles as the GC
	// has more resources behind it, lessening costs from the write barrier,
	// but comes at the cost of increasing mutator latency.
	gcGoalUtilization = gcBackgroundUtilization

	// gcBackgroundUtilization is the fixed CPU utilization for background

	var lastKey []byte
	var hasMore bool
	var getResp *clientv3.GetResponse
	var numFetched int
	var numEvald int
	// Because these metrics are for understanding the costs of handling LIST requests,
	// get them recorded even in error cases.
	defer func() {
		numReturn := v.Len()
		metrics.RecordStorageListMetrics(s.groupResourceString, numFetched, numEvald, numReturn)
	}()

                n++;
                int size = chain.size();
                int cost = chain.getCreditCost();
                CommonServerMessageBlockRequest next = chain.getNext();
                if ( log.isTraceEnabled() ) {
                    log.trace(
                        String.format("%s costs %d avail %d (%s)", chain.getClass().getName(), cost, this.credits.availablePermits(), this.name));
                }

			}
		}
	}
	// Update the data cache.
	hosts.expire = now.Add(cacheMaxAge)
	hosts.path = hp
	hosts.byName = hs
	hosts.byAddr = is
	hosts.mtime = mtime
	hosts.size = size
}

// lookupStaticHost looks up the addresses and the canonical name for the given host from /etc/hosts.
func lookupStaticHost(host string) ([]string, string) {
	hosts.Lock()
	defer hosts.Unlock()
	readHosts()
	if len(hosts.byName) != 0 {

// entries in the next level which each represent 256 MiB regions, and so on.
//
// Thus, this design only scales to heaps so large, but can always be extended to
// larger heaps by simply adding levels to the radix tree, which mostly costs
// additional virtual address space. The choice of managing large arrays also means
// that a large amount of virtual address space may be reserved by the runtime.

package runtime

import (
	"internal/runtime/atomic"

namespace mlir {
namespace TFL {
namespace tac {

// Cost attribute string on the TFL dialect.
constexpr char kCost[] = "tac.cost";

inline void UpdateCost(Operation* op, float cost, OpBuilder* builder) {
  op->setAttr(kCost, builder->getF32FloatAttr(cost));
}

// Get the cost annotated with kCost.
inline bool GetCostOnOp(Operation* op, float* cost) {
  auto cost_type = op->getAttrOfType<FloatAttr>(kCost);

		var cost uint64
		if len(args) > 0 {
			cost += traversalCost(args[0]) // these O(n) operations all cost roughly the cost of a single traversal
		}
		return &cost
	case "url", "lowerAscii", "upperAscii", "substring", "trim":
		if len(args) >= 1 {
			cost := uint64(math.Ceil(float64(actualSize(args[0])) * common.StringTraversalCostFactor))
			return &cost
		}
	case "replace", "split":
		if len(args) >= 1 {

// RUN: tac-opt-all-backends -tfl-get-op-cost %s -split-input-file -verify-diagnostics | FileCheck %s

func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  // CHECK: tac.cost = 7.864320e+05
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>

// On any error, it conservatively returns true.
//
// This exists just to eliminate work on the builders, since compiling
// a test in race mode just to discover it has no benchmarks costs a
// second or two per package, and this function returns false for
// about 100 packages.
func (t *tester) packageHasBenchmarks(pkg string) bool {
	pkgDir := filepath.Join(goroot, "src", pkg)

// RUN: tac-opt-all-backends -tfl-compute-cost %s -split-input-file -verify-diagnostics | FileCheck %s

// CHECK: tac.cost = 7.864320e+05
func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>