} else if err != nil {
		return
	}
	// Check that OOM scores were applied to the right processes.
	if len(appliedPids) != len(pidOOMs) {
		t.Errorf("Applied OOM scores to incorrect number of processes - %+v vs %v", appliedPids, pidOOMs)
		return
	}
	for _, pid := range appliedPids {
		if !pidOOMs[pid] {
			t.Errorf("Failed to apply OOM scores to process %d", pid)
		}
	}
}

func TestOOMScoreAdjContainer(t *testing.T) {

  LazyLogger(Class<?> ownerOfLogger) {
    this.loggerName = ownerOfLogger.getName();
  }

  Logger get() {
    /*
     * We use double-checked locking. We could the try racy single-check idiom, but that would
     * depend on Logger not contain mutable state.
     *
     * We could use Suppliers.memoizingSupplier here, but I micro-optimized to this implementation

  LazyLogger(Class<?> ownerOfLogger) {
    this.loggerName = ownerOfLogger.getName();
  }

  Logger get() {
    /*
     * We use double-checked locking. We could the try racy single-check idiom, but that would
     * depend on Logger not contain mutable state.
     *
     * We could use Suppliers.memoizingSupplier here, but I micro-optimized to this implementation

				// Handle request r using config c.
				_, _ = r, c
			}
		}()
	}
}

// The following example shows how to maintain a scalable frequently read,
// but infrequently updated data structure using copy-on-write idiom.
func ExampleValue_readMostly() {
	type Map map[string]string
	var m atomic.Value
	m.Store(make(Map))
	var mu sync.Mutex // used only by writers

			u := evalOrig(v, ancestor, semi, label)
			if semi[u] < semi[v] {
				idom[v] = fromID[u]
			} else {
				idom[v] = fromID[parent[w]]
			}
		}
	}
	// step 4 in toplas paper
	for i := ID(2); i <= n; i++ {
		w := vertex[i]
		if idom[w].ID != vertex[semi[w]] {
			idom[w] = idom[idom[w].ID]
		}
	}

	return idom
}

  @Override
  public ImmutableBiMap<V, K> inverse() {
    if (inverse != null) {
      return inverse;
    } else {
      // racy single-check idiom
      ImmutableBiMap<V, K> result = lazyInverse;
      if (result == null) {
        return lazyInverse = new SingletonImmutableBiMap<>(singleValue, singleKey, this);
      } else {
        return result;
      }

  constexpr int64_t dims[] = {100, 100};
  constexpr int num_elements = dims[0] * dims[1];
  float data[num_elements];
  for (int i = 0; i < num_elements; ++i) {
    data[i] = 1.0f;
  }
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_FLOAT, &dims[0],
                                        sizeof(dims) / sizeof(int64_t), status);

  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xi32>) -> tensor<3x2xi32>
  return %1 : tensor<3x2xi32>
}
// CHECK: transpose

// -----

// Tests that transposing an argument cannot be folded.

// CHECK-LABEL: transpose_arg
func.func @transpose_arg(%arg0: tensor<2x3xf32>) -> tensor<3x2xf32> {
  %0 = stablehlo.transpose %arg0, dims = [1, 0] : (tensor<2x3xf32>) -> tensor<3x2xf32>

	c := testConfig(t)
	fun := c.Fun("entry",
		Bloc("entry",
			Valu("mem", OpInitMem, types.TypeMem, 0, nil),
			Exit("mem")))

	doms := map[string]string{}

	CheckFunc(fun.f)
	verifyDominators(t, fun, dominators, doms)
	verifyDominators(t, fun, dominatorsSimple, doms)

}

func TestDominatorsSimple(t *testing.T) {
	c := testConfig(t)
	fun := c.Fun("entry",
		Bloc("entry",

	// amd64:-`.*runtime\.slicebytetostring\(`
	return m[[2]string{0: string(bytes)}]
}

// ------------------- //
//     Map Clear       //
// ------------------- //

// Optimization of map clear idiom (Issue #20138).

func MapClearReflexive(m map[int]int) {
	// amd64:`.*runtime\.mapclear`
	// amd64:-`.*runtime\.mapiterinit`
	for k := range m {
		delete(m, k)
	}
}

func MapClearIndirect(m map[int]int) {