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 * The class set abstraction effectively creates a key for all classes involved in a type.
 * The {@link WeakClassSet#isCollected()} method returns true when any of the classes involved have been collected.
 * All keys (and associated entries) are removed from the map by the {@link #cleanUp()} method, which should be invoked periodically to trim the cache of no longer needed data.
 */
public class ModelSchemaCache {

		{
			name:                     "involved object name should be included in source+object key",
			serverBurst:              100,
			sourceAndObjectBurst:     1,
			sourceAndObjectCacheSize: 10,
			requests: createSourceAndObjectKeyInclusionRequests(func(label string) *api.Event {
				return &api.Event{InvolvedObject: api.ObjectReference{Name: label}}
			}),
		},
		{

 * limitations under the License.
 */

/**
 * <p>Gradle's build language API, which is available from your build files.
 * Location of the main interfaces involved in build scripts.</p>
 */
@org.gradle.api.NonNullApi

namespace mlir {
namespace TF {

inline constexpr char kStandardPipelineBefore[] = "standard_pipeline_before";
inline constexpr char kStandardPipelineAfter[] = "standard_pipeline_after";

// Runs all passes involved in transforming or optimizing an MLIR graph without
// any target specialization. When enable_logging is true, enables
// tensorflow::BridgeLogger. When enable_inliner is true, enables the inliner
// pass.
ABSL_DEPRECATED(

This program, dist, is the bootstrapping tool for the Go distribution.

As of Go 1.5, dist and other parts of the compiler toolchain are written
in Go, making bootstrapping a little more involved than in the past.
The approach is to build the current release of Go with an earlier one.

The process to install Go 1.x, for x ≥ 22, is:

1. Build cmd/dist with Go 1.20.6.
2. Using dist, build Go 1.x compiler toolchain with Go 1.20.6.

void RegisterTPULowerClusterToRuntimeOpsPassPipeline() {
  static mlir::PassPipelineRegistration<StandardPipelineOptions> pipeline(
      "tfrt-lower-cluster-to-runtime-ops-tpu",
      "Run all the passes involved after the clustering transformations from "
      "the TF2XLA Bridge. Takes as input a Module with tf_device.cluster ops "
      "and outputs TFRT runtime ops such as TPUCompile. This pipeline is for "
      "TPU.",

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v1 {

// Run all the passes involved in transforming the graph before execution so
// that it is suitable for targeting devices when called via the TF1 Session
// API.
// These transformations take as input a Tensorflow Graph as an MLIR Module

#  2) ./tools/docker calls `make build.docker.x` targets to compute the dependencies required
#  3) ./tools/docker triggers the actual docker commands required
# As a result, there are two layers of make involved.

docker: ## Build all docker images
	./tools/docker

docker.save: ## Build docker images and save to tar.gz
	./tools/docker --save

docker.push: ## Build all docker images and push to
	./tools/docker --push