TF_ASSIGN_OR_RETURN(xla::ProgramShape program_shape,
                          result.computation->GetProgramShape());
      xla::HloModuleConfig config(program_shape);
      TF_ASSIGN_OR_RETURN(
          std::unique_ptr<xla::HloModule> new_module,
          xla::HloModule::CreateFromProto(result.computation->proto(), config));

      xla::HloPrintOptions opts;
      if (stage == IrExportStage::HLO_NO_METADATA) {

type ConfigGenerator interface {
	// BuildListeners returns the list of inbound/outbound listeners for the given proxy. This is the LDS output
	// Internally, the computation will be optimized to ensure that listeners are computed only
	// once and shared across multiple invocations of this function.
	BuildListeners(node *model.Proxy, push *model.PushContext) []*listener.Listener

   *
   * <p>The {@link OutputStream} should not be written to before or after the hand-off.
   */
  // TODO(user): Evaluate whether it makes sense to always piggyback the computation of a
  // HashCode on an existing OutputStream, compared to creating a separate OutputStream that could
  // be (optionally) be combined with another if needed (with something like
  // MultiplexingOutputStream).

public final class Quantiles {

  /** Specifies the computation of a median (i.e. the 1st 2-quantile). */
  public static ScaleAndIndex median() {
    return scale(2).index(1);
  }

  /** Specifies the computation of quartiles (i.e. 4-quantiles). */
  public static Scale quartiles() {
    return scale(4);
  }

  /** Specifies the computation of percentiles (i.e. 100-quantiles). */

                .setTargetBytecodeVersion(targetBytecodeVersion)
                .setJdk(DefaultInstalledJdk.current()));
    }

    // Simulates computation of the IdeaProject language level property in the IdeaPlugin
    private static IdeaLanguageLevel resolveRootLanguageLevel(IdeaProjectInternal ideaProjectExt, List<IsolatedIdeaModuleInternal> isolatedModules) {

    mlir::TF::RuntimeDevices devices, std::string* host_device);

// Parses XLA compilation and execution devices from a tf_device.cluster and
// returns the host device for the head and tail computations. For TPU device,
// if the computation is replicated, GetDeviceAliasForHostOfLogicalCore(0) is
// returned instead.
mlir::LogicalResult GetHostDeviceOutsideComputation(
    mlir::TF::RuntimeDevices devices, mlir::tf_device::ClusterOp cluster,

# out if the fake clang doesn't work.
env CGO_ENABLED=1

# The bug in https://go.dev/issue/64423 resulted in cache keys that
# didn't contain any information about the C compiler.
# Since the bug was in cache key computation, isolate the cache:
# if we change the way caching works, we want the test to fail
# instead of accidentally reusing the cached information from a
# previous test run.
env GOCACHE=$WORK${/}.cache
mkdir $GOCACHE

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFTPU {

namespace {

// This pass removes Identity/IdentityN ops from the TPU computation and
// reachable functions.
// TODO(lyandy): Remove this pass once resource op lifting is migrated to use
// resource alias analysis and support region based control flow. Removing

   *     ExecutionException} is thrown <a
   *     href="https://github.com/google/guava/wiki/CachesExplained#interruption">even if
   *     computation was interrupted by an {@code InterruptedException}</a>.)
   * @throws UncheckedExecutionException if an unchecked exception was thrown while loading the
   *     value

import java.nio.ByteOrder;
import java.nio.charset.Charset;
import java.util.Arrays;

/**
 * Skeleton implementation of {@link HashFunction}, appropriate for non-streaming algorithms. All
 * the hash computation done using {@linkplain #newHasher()} are delegated to the {@linkplain
 * #hashBytes(byte[], int, int)} method.
 *
 * @author Dimitris Andreou
 */
@Immutable
@ElementTypesAreNonnullByDefault