For a 2n-digit number divided by an n-digit number (the worst size-n case for
division complexity), this algorithm uses n+1 iterations, each of which must do
at least the 1-by-n-digit multiplication q̂·v. That's O(n) iterations of
O(n) time each, so O(n²) time overall.


Recursive Division

For very large inputs, it is possible to improve on the O(n²) algorithm.

          "(experimental) "
          "Limit the operations clustered by XLA to these operations. "
          "If multiple, separate them with commas. Shortcuts: "
          " PW: All point-wise operations."
          " RED: All reduction operations."
          " MISC: Mixed operations."
          " PWRED: TF operations that get converted to PW+RED operation in XLA."
          " REDUCEWINDOW: TF operations like MaxPool/AvgPool that get "

                }
              },
              directExecutor());
      for (int i = 0; i < 50_000; i++) {
        if (i % DIRECT_EXECUTIONS_PER_THREAD == 0) {
          // after some number of iterations, switch threads
          unused =
              serializer.submit(
                  new Callable<@Nullable Void>() {
                    @Override
                    public @Nullable Void call() {

  let summary = "Asynchronously execution of while op for tf_mlrt";
  let description = [{
    cond: The boolean to control whether the first iteration should be executed.
    arguments: the last $immutable_size elements are invariants between iterations.
    results: a list of futures.

     * with each iteration, so this algorithm takes O(log(digits)) iterations.
     *
     * We start out with a double-precision approximation, which may be higher or lower than the
     * true value. Therefore, we perform at least one Newton iteration to get a guess that's
     * definitely >= floor(sqrt(x)), and then continue the iteration until we reach a fixed point.
     */
    BigInteger sqrt0;
    int log2 = log2(x, FLOOR);

	// predecessors have been visited.
	po := state.f.Postorder()
	converged := false

	// The iteration rule is that by default, run until converged, but
	// if a particular iteration count is specified, run that many
	// iterations, no more, no less.  A count is specified as the
	// thousands digit of the location lists debug flag,
	// e.g. -d=locationlists=4000

}
"""

        when:
        args "--configure-on-demand", "--parallel"
        execute(buildA, ":c:resolve", ":d:resolve")

        then:
        noExceptionThrown()

        where:
        iterations << (0..20).collect()
    }

    @Issue("https://github.com/gradle/gradle/issues/15068")
    def "can develop plugin whose build requires dependency resolution using configure-on-demand"() {
        given:

				fmt.Fprintf(f.w, "To re-run:\ngo test -run=%s/%s\n", f.name, testName)
			}
		}
		// TODO(jayconrod,katiehockman): Aggregate statistics across workers
		// and add to FuzzResult (ie. time taken, num iterations)

	case fuzzWorker:
		// Fuzzing is enabled, and this is a worker process. Follow instructions
		// from the coordinator.
		if err := f.fuzzContext.deps.RunFuzzWorker(func(e corpusEntry) error {

def TensorListOpsDecompositionPass : Pass<"tf-tensor-list-ops-decomposition", "ModuleOp"> {
  let summary = "Decomposes TensorList operations into generic operations on tensors.";

  let description = [{
This pass rewrites TensorList operations into generic and non-mutating
operations on tensors. This results in operations that can be legalized to XLA.

The list is converted to a single large tensor that includes all list elements,

    runLeastOfComparison(10, 30, 2);
  }

  private static void runLeastOfComparison(int iterations, int elements, int seeds) {
    Random random = new Random(42);
    Ordering<Integer> ordering = Ordering.natural();

    for (int i = 0; i < iterations; i++) {
      List<Integer> list = Lists.newArrayList();
      for (int j = 0; j < elements; j++) {