// is we'd need to wait on the result of a create to record the pod's
		// UID, which would require locking *across* the create, which will turn
		// into a performance bottleneck. We should generate a UID for the pod
		// beforehand and store it via ExpectCreations.
		rsc.expectations.ExpectCreations(logger, rsKey, diff)
		logger.V(2).Info("Too few replicas", "replicaSet", klog.KObj(rs), "need", *(rs.Spec.Replicas), "creating", diff)

    OpVisitorState op_to_visit = ops_to_visit.pop_back_val();
    Operation* curr_op = op_to_visit.op;

    Value token = op_to_visit.token;
    // Ops may be removed, so the next op is kept track of beforehand.
    Operation* next_op = curr_op->getNextNode();

    if (auto host_compute = dyn_cast<TF::_XlaHostComputeMlirOp>(curr_op)) {
      token = RewriteHostComputeOp(builder, host_compute, token);

                                    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.

	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]`.