},
								},
							},
						},
					},
					SharedWithUpstream: sfsvalue.FilterStateValue_ONCE,
				}},
			}),
		},
	}
)

// Router is used a bunch, so its worth precomputing even though we have a few options.
// Since there are only 4 possible options, just precompute them all
var routers = func() map[RouterFilterContext]*hcm.HttpFilter {
	res := map[RouterFilterContext]*hcm.HttpFilter{}

  // Stream associated with this device. Operations enqueued on this
  // stream are executed on the device. Operations include data
  // copying back and forth between CPU and the device, and
  // computations enqueued by XLA.
  std::shared_ptr<se::Stream> stream_ TF_GUARDED_BY(mu_);
  // If false, only stream_ is valid and all computation and transfers use

For example, if the Ant build follows a fairly standard approach for compilation, static resources, packaging and unit tests, then it is probably worth migrating all of those together.
But if the build performs some extra processing on the compiled classes, or does something unique when processing the static resources, it is probably worth splitting those tasks into separate stages.

[[migant:managing_dependencies]]
== Managing dependencies

 *   windowsStartScriptGenerator.template = resources.text.fromFile('customWindowsStartScript.txt')
 * }
 * </pre>
 */
@DisableCachingByDefault(because = "Not worth caching")
public abstract class CreateStartScripts extends ConventionTask {

    private File outputDir;
    private String executableDir = "bin";
    private final Property<String> mainModule;

            tasks.register<CheckDeps>("checkDeps") {
                files.from(configurations.compileClasspath)
                expected.set(listOf("lib-core-1.0.jar", "lib-ext-1.0.jar"))
            }
            // Might be worth checking constraints too? Not sure if necessary because the Groovy DSL version covers that
            // and the selected versions above would be wrong.
        """

        expect:
        succeeds ':checkDeps'

 * if any intersection is found. The sizes of both arguments are assumed to be so small, and the
 * likelihood of an intersection so great, that it is not worth the CPU cost of sorting or the
 * memory cost of hashing.
 */
internal fun Array<String>.hasIntersection(
  other: Array<String>?,
  comparator: Comparator<in String>,
): Boolean {

				// (as is common) or a high bit (as commonly implemented on MIPS
				// hardware before around 2012). We believe that the increase in clarity
				// from identifying "NaN" with math.NaN() is worth the slight ambiguity
				// from a platform-dependent value.
				fmt.Fprintf(b, "math.Float32frombits(0x%x)\n", math.Float32bits(t))
			} else {
				// We encode all other values — including the NaN value that is

		return errors.New("bad")
	}
	funcs.Store(pt)

	// for now, we just make sure this doesn't crash. To be totally correct, we'd need to capture stderr and
	// inspect it, but it's just not worth it
	defaultScope.Error("TestBadWriter")
}

func BenchmarkLog(b *testing.B) {
	run := func(name string, f func()) {
		b.Run(name, func(b *testing.B) {
			o := testOptions()

                   GraphShapeInfo* shape_info) {
  ShapeRefiner shape_refiner(graph->versions(), graph->op_registry());
  shape_refiner.set_require_shape_inference_fns(false);
  // TODO(dlibenzi): Verify if it is worth trying to infer shaped within
  // functions. Some functions can be called at multiple locations with
  // difference shapes, which will trigger a shape inference based on the
  // arguments passed at the first call.

You will get completion in code you might even consider impossible before. As for example, the `price` key inside a JSON body (that could have been nested) that comes from a request.

No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`.

### Short