METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8) {
      // Skipping input type check for weight-only quantization as it can be
      // dequantized beforehand for the legacy scheme.
      has_quantized_types = true;
    } else {
      // Determines if all required float input/outputs are now quantized.
      // Either one of the criteria needs to meet.

                                          BoolAttr adj_y) {
  // TensorFlow BatchMatMulOp allows the batch dimensions to be broadcastable
  // while the XlaDotV2Op doesn't. So we have to broadcast them beforehand.
  BroadcastBatchDimensionsForBatchMatMul(builder, loc, input, weight);

  // Both input and weight have the same rank after broadcasting.
  ShapedType weight_shape = mlir::cast<ShapedType>(weight.getType());

----
$ gradle init --type java-library
----

=== Standardize and provision Gradle
The built-in `gradle wrapper` task generates a script, `gradlew`, that invokes a declared version of Gradle, downloading it beforehand if necessary.

----
$ gradle wrapper --gradle-version=8.1
----

func ReadCPUStats() CPUStats {
	return work.cpuStats
}

func ReadMetricsSlow(memStats *MemStats, samplesp unsafe.Pointer, len, cap int) {
	stw := stopTheWorld(stwForTestReadMetricsSlow)

	// Initialize the metrics beforehand because this could
	// allocate and skew the stats.
	metricsLock()
	initMetrics()

	systemstack(func() {
		// Donate the racectx to g0. readMetricsLocked calls into the race detector
		// via map access.

+
This can be replaced by an inter-project task dependency in the corresponding Gradle build script, as demonstrated in the following example that assumes the "web" project's "compile" task is requires "util" to be built beforehand:
+
====
include::sample[dir="snippets/antMigration/multiProject/kotlin",files="web/build.gradle.kts[]"]
include::sample[dir="snippets/antMigration/multiProject/groovy",files="web/build.gradle[]"]
====
+

e&&(r=e,e=null),St(t,e,n,r)}}function St(n,t,e,r){function u(){var n,t=c.status;if(!t&&c.responseText||t>=200&&300>t||304===t){try{n=e.call(i,c)}catch(r){return o.error.call(i,r),void 0}o.load.call(i,n)}else o.error.call(i,c)}var i={},o=Xo.dispatch("beforesend","progress","load","error"),a={},c=new XMLHttpRequest,s=null;return!Go.XDomainRequest||"withCredentials"in c||!/^(http(s)?:)?\/\//.test(n)||(c=new XDomainRequest),"onload"in c?c.onload=c.onerror=u:c.onreadystatechange=function(){c.readyState>3&...

----
====

You can of course just add an explicit task dependency via `dependsOn`, but the above approach provides more semantic meaning, explaining why `compileJava` has to run beforehand.

[[sec:disable-state-tracking]]
=== Disabling up-to-date checks

Gradle automatically handles up-to-date checks for output files and directories, but what if the task output is something else entirely?

d(n,this)}},insertAdjacentHTML:function(e,t){var n,r;switch(String(e).toLowerCase()){case"beforebegin":n=this.parentNode,r=this;break;case"afterend":n=this.parentNode,r=this.nextSibling;break;case"afterbegin":n=this,r=this.firstChild;break;case"beforeend":n=this,r=null;break;default:return}var o=u(n,t);n.insertBefore(o,r)},get hidden(){return this.hasAttribute("hidden")},set hidden(e){e?this.setAttribute("hidden",""):this.removeAttribute("hidden")}}),["clientHeight","clientLeft","clientTop","cli...

	rex &= 0x40 | Rxr
	if a.Offset != int64(int32(a.Offset)) {
		// The rules are slightly different for 386 and AMD64,
		// mostly for historical reasons. We may unify them later,
		// but it must be discussed beforehand.
		//
		// For 64bit mode only LEAL is allowed to overflow.
		// It's how https://golang.org/cl/59630 made it.
		// crypto/sha1/sha1block_amd64.s depends on this feature.
		//

*   Hot-fix was needed for an issue affecting the TensorFlow installation
    process.
    *   TensorFlow 2.15.0 Python package was requesting `tensorrt`-related
        packages that cannot be found unless the user installs them beforehand
        or provides additional installation flags.
    *   This dependency affected anyone installing TensorFlow 2.15 alongside
        NVIDIA CUDA dependencies via `pip install tensorflow[and-cuda]`.