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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 {

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(

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

     * if (this is String) {
     *   this.substring() // 'this' receiver is explicit, so no implicit smart-cast here.
     *
     *   smartcast() // 'this' receiver is implicit, therefore there is implicit smart-cast involved.
     * }
     * ```
     */
    public fun KtExpression.getImplicitReceiverSmartCast(): Collection<KaImplicitReceiverSmartCast> =

    }
    integTestDistributionRuntimeOnly(project(":distributions-full")) {
        because("Includes tests for builds with the enterprise plugin and TestKit involved; ConfigurationCacheJacocoIntegrationTest requires JVM distribution")
    }
    crossVersionTestDistributionRuntimeOnly(project(":distributions-core"))
}

packageCycles {

#include "tensorflow/compiler/mlir/tf2xla/api/v2/device_type.pb.h"
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v2 {

// Run all the passes involved in transforming the graph before execution so
// that it is suitable for targeting devices when called with the TF 2 Function
// API. Users that need clustering with the Session API should use the v1 Bridge

        // A better solution for output files would be to record the type of the output file and then using this type here instead of looking at the disk.
        // Though that is more involved and as soon as the file has been produced, the right file type will be detected here as well.
        boolean elementIsFile = !element.isDirectory();