When you have a fully up to date build and you clean and re-run the code generator task on the same code base it should generate _exactly the same output_, so anything that depends on that output will stay up-to-date.

It might also be that your code generator adds some extra information to its output that doesn't depend on its declared inputs, like a timestamp.

        when:
        withBuildCache().run "help"
        then:
        result.groupedOutput.task(":buildSrc:compileGroovy").outcome == "FROM-CACHE"
    }

    def "tasks stay cached after buildSrc with custom Groovy task is rebuilt"() {
        configureCacheForBuildSrc()
        file("buildSrc/src/main/groovy/CustomTask.groovy") << defineCachedTask()
        file("input.txt") << "input"

		return
	}

	// only run one worker
	go wait.Until(c.runWorker, time.Second, ctx.Done())

	// checks are cheap.  run once a minute just to be sure we stay in sync in case fsnotify fails again
	// start timer that rechecks every minute, just in case.  this also serves to prime the controller quickly.
	_ = wait.PollImmediateUntil(1*time.Minute, func() (bool, error) {

	a.AuthenticateToken(context.Background(), "bad3")
	fakeClock.Step(2 * time.Microsecond)
	if !reflect.DeepEqual(calledWithToken, []string{"bad1", "bad2", "bad3", "bad1", "bad2", "bad3"}) {
		t.Errorf("Expected failing calls to not stay in the cache, got %v", calledWithToken)
	}

	// reset calls, make the backend return success for three user tokens
	calledWithToken = []string{}
	resultUsers, resultOk, resultErr = map[string]user.Info{}, true, nil

Note that Zinc's Nailgun based daemon mode is not supported. Instead, we plan to enhance Gradle's own compiler daemon to stay alive across Gradle invocations, reusing the same Scala compiler. This is expected to yield another significant speedup for Scala compilation.

[[sec:eclipse_integration]]
== Eclipse Integration

  let constructor = "CreateOptimizeOpOrderPass()";
}

def PartitionedTopologicalSortPass : Pass<"tfl-partitioned-topological-sort", "mlir::func::FuncOp"> {
  let summary = "Re-sort execution order such that delegated ops stay together";
  let constructor = "CreatePartitionedTopologicalSortPass()";
    let description = [{
      This transformation reorders operations such that operations that will be

Furthermore, the process will be a _worker daemon_ that will stay alive and can be reused for future work items with the same requirements.

	i := len(z)
	for i > 0 && z[i-1] == 0 {
		i--
	}
	return z[0:i]
}

func (z nat) make(n int) nat {
	if n <= cap(z) {
		return z[:n] // reuse z
	}
	if n == 1 {
		// Most nats start small and stay that way; don't over-allocate.
		return make(nat, 1)
	}
	// Choosing a good value for e has significant performance impact
	// because it increases the chance that a value can be reused.
	const e = 4 // extra capacity

// of ops with the trait keep their order to all ops with unknown side effects
// (e.g. `stateful` ops). For a `write` effect, all instances of ops with the
// trait stay in order, and they also keep their order to all unknown side-
// effecting ops. Note that for `read` effects ops might be pruned if nothing
// depends on them.

//
// A block argument can also become a member (or even a root member), however
// only operations will become a part of the outline `tf_device.cluster`, block
// arguments will stay as block arguments, and will later become cluster
// function inputs.
struct Member {
  Member(unsigned root, Source source, Operation *insertion_point,
         ValuesConstraintSet constraints = {})
      : root(root),