// Nathan Kurz arXiv:1902.01961
	//
	// To minimize the risk of introducing errors, we implement the
	// algorithm exactly as stated, rather than trying to adapt it to
	// fit typical Go idioms.
	N := bits.Len(uint(max))
	var F int
	if powerOfTwo(d) {
		F = int(math.Log2(float64(d)))
		if d != 1<<F {
			panic("imprecise log2")
		}
	} else {
		for L := 0; ; L++ {

    left_padding = split_outputs[0]
    core = split_outputs[1]
    right_padding = split_outputs[2]
    reversed_left_padding = tf.raw_ops.Reverse(tensor=left_padding, dims=rdims)
    reversed_right_padding = tf.raw_ops.Reverse(
        tensor=right_padding, dims=rdims)
    zero_like = tf.raw_ops.ZerosLike(x=core)
    left_offset, _ = tf.raw_ops.SplitV(
        value=zero_like,
        size_splits=[-1, left_padding_size],

		t.Fatal(err)
	}
	defer f.Close()
	defer Remove(f.Name())

	msg := "Programming today is a race between software engineers striving to " +
		"build bigger and better idiot-proof programs, and the Universe trying " +
		"to produce bigger and better idiots. So far, the Universe is winning."

	if err := WriteFile(f.Name(), []byte(msg), 0644); err != nil {
		t.Fatalf("WriteFile %s: %v", f.Name(), err)
	}

	if err != nil {
		t.Fatal(err)
	}
	filename := f.Name()
	data := "Programming today is a race between software engineers striving to " +
		"build bigger and better idiot-proof programs, and the Universe trying " +
		"to produce bigger and better idiots. So far, the Universe is winning."

	if err := WriteFile(filename, []byte(data), 0644); err != nil {
		t.Fatalf("WriteFile %s: %v", filename, err)
	}

	} 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;
      }