// XlaCompilationResult pointed to by `result`.
  virtual Status Compile(const XlaCompiler::CompileOptions& options,
                         const NameAttrList& function,
                         absl::Span<const XlaArgument> args,
                         XlaCompilationResult* result) = 0;

  // Compiles a Tensorflow single op to an HloModuleProto stored in the
  // XlaCompilationResult pointed to by `result`.

    <a class="badge" href="https://dpeuniversity.gradle.com/app/courses/ec69d0b8-9171-4969-ac3e-82dea16f87b0/" target="_blank">
        <span class="badge-type button--blue">LEARN</span>
        <span class="badge-text">Incremental Builds and Build Caching with Gradle&nbsp;&nbsp;&nbsp;&gt;</span>
    </a>
</div>
++++

Gradle uses two main features to reduce build time: *incremental builds* and *build caching*.

    <a class="badge" href="https://dpeuniversity.gradle.com/app/courses/ec69d0b8-9171-4969-ac3e-82dea16f87b0/" target="_blank">
        <span class="badge-type button--blue">LEARN</span>
        <span class="badge-text">Incremental Builds and Build Caching with Gradle&nbsp;&nbsp;&nbsp;&gt;</span>
    </a>
</div>
++++

Learn the basics of Gradle's incremental builds.

****
**In this section you will:**

			continue
		}
		obj, span, objIndex := findObject(ptr, 0, 0)
		if obj == 0 {
			continue
		}
		// TODO: Consider making two passes where the first
		// just prefetches the mark bits.
		mbits := span.markBitsForIndex(objIndex)
		if mbits.isMarked() {
			continue
		}
		mbits.setMarked()

		// Mark span.
		arena, pageIdx, pageMask := pageIndexOf(span.base())

#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

Status PickDeviceHelper(bool allow_mixing_unknown_and_cpu,
                        absl::Span<const absl::string_view> device_names,
                        string* result) {
  jit::DeviceInfoCache cache;
  jit::DeviceSet device_set;
  for (absl::string_view name : device_names) {

  // XlaCompilationResult pointed to by `result` by calling
  // XlaCompiler::CompileFunction.
  Status Compile(const XlaCompiler::CompileOptions& options,
                 const NameAttrList& function,
                 absl::Span<const XlaArgument> args,
                 XlaCompilationResult* result) override;

  // Compiles a Tensorflow single op into an HloModuleProto stored in the
  // XlaCompilationResult pointed to by `result` by calling

	arch string
	name string
}

var (
	bases   = map[baseKey]v1.Image{}
	basesMu sync.RWMutex
)

func WarmBase(ctx context.Context, architectures []string, baseImages ...string) {
	_, span := tracing.Start(ctx, "RunCrane")
	defer span.End()
	basesMu.Lock()
	wg := sync.WaitGroup{}
	wg.Add(len(baseImages) * len(architectures))
	resolvedBaseImages := make([]v1.Image, len(baseImages)*len(architectures))
	keys := []baseKey{}

#include <string>
#include <utility>

#include "absl/container/flat_hash_map.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/str_cat.h"
#include "absl/types/span.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"

namespace stablehlo::quantization {

#include "tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_collector_min_max.h"

#include <algorithm>
#include <cstdint>
#include <limits>
#include <optional>

#include "absl/types/span.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics.pb.h"

namespace tensorflow {
namespace calibrator {

void CalibrationStatisticsCollectorMinMax::ClearData() {

namespace tensorflow {
namespace gradients {
namespace {
class IdentityNGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    for (int i = 0; i < grad_outputs.size(); i++) {
      auto grad_input = grad_outputs[i];