tt := tt
				t.Run(tt.name, func(t *testing.T) {
					if tt.oldestWins {
						test.SetForTest(t, &features.PersistOldestWinsHeuristicForVirtualServiceHostMatching, true)
					} else {
						t.Parallel() // feature flags and parallel tests don't mix
					}
					cfg := knownServices
					for _, tc := range tt.cfg {
						cfg = cfg + "\n---\n" + tc.Config(t, variant)
					}
					istio, k, err := crd.ParseInputs(cfg)

// license that can be found in the LICENSE file.

// Garbage collector (GC).
//
// The GC runs concurrently with mutator threads, is type accurate (aka precise), allows multiple
// GC thread to run in parallel. It is a concurrent mark and sweep that uses a write barrier. It is
// non-generational and non-compacting. Allocation is done using size segregated per P allocation

	metadataShallowVersions := make([][]xlMetaV2ShallowVersion, len(rawFileInfos))
	var v2bufs [][]byte
	if !readData {
		v2bufs = make([][]byte, len(rawFileInfos))
	}

	// Read `xl.meta` in parallel across disks.
	for index := range rawFileInfos {
		rf := rawFileInfos[index]
		if rf.Buf == nil {
			continue
		}
		if !readData {
			// Save the buffer so we can reuse it.
			v2bufs[index] = rf.Buf

	// Cache these type symbols, so as to avoid repeatedly looking them up
	typeRuntimeEface loader.Sym
	typeRuntimeIface loader.Sym
	uintptrInfoSym   loader.Sym

	// Used at various points in that parallel portion of DWARF gen to
	// protect against conflicting updates to globals (such as "gdbscript")
	dwmu *sync.Mutex
}

// dwSym wraps a loader.Sym; this type is meant to obey the interface

Configuration time will still be spent the first time you run a particular set of tasks.
However, you should see build performance improvement immediately because <<#config_cache:intro:performance_improvements,tasks will run in parallel>>.

	mutex sync.Mutex

	// The indices of all claims that:
	// - are allocated
	// - use delayed allocation or the builtin controller
	// - were not available on at least one node
	//
	// Set in parallel during Filter, so write access there must be
	// protected by the mutex. Used by PostFilter.
	unavailableClaims sets.Set[int]

	informationsForClaim []informationForClaim
}

    string dst_func_id;
    TF_RETURN_IF_ERROR(GetFunctionNameAttr(edge->dst(), &dst_func_id));

    // Ignore edges that are strictly contained within one subgraph, unless
    // we are constructing parallel check graphs.
    if (IsInSubgraph(src_func_id) && IsInSubgraph(dst_func_id) &&
        src_func_id == dst_func_id) {
      continue;
    }

						}

						if denyNotFound {
							name = name + "DenyNotFound"
						} else {
							name = name + "AllowNotFound"
						}

						t.Run(name, func(t *testing.T) {
							t.Parallel()
							// Test creating a policy with a cluster or namesapce-scoped param
							// and binding with the provided configuration. Test will ensure
							// that the provided configuration is capable of matching

  // on GPU1. However, it should not be clustered with the previous node on
  // GPU1, because that will serialize production of its inputs that should be
  // done in parallel.
  //
  // This graph is:
  // (Const0, Const0) -> MatMul0
  // (Const1, Const1) -> MatMul1
  // (MatMul0, MatMul1) -> MatMulCombined
  //
  // Device0: [Const0, Const0, MatMul0]

	}

	// Convert map to list.
	var result []*kubecontainer.Pod
	for _, pod := range pods {
		result = append(result, pod)
	}

	// There are scenarios where multiple pods are running in parallel having
	// the same name, because one of them have not been fully terminated yet.
	// To avoid unexpected behavior on container name based search (for example