FunctionDef *xla_fdef = fdl.add_function();
    TF_CHECK_OK(GraphToFunctionDef(*g, "f3", xla_fdef));
  }
  FunctionLibraryDefinition fld(OpRegistry::Global(), fdl);

  // Build the XLA computation graph.
  // "arg0" (T=DT_RESOURCE), "arg1" (T=DT_INT32)
  // "arg0", "arg1" -> "if" (If) -> "ret0", "ret1"
  // "arg0", "arg1" -> "while" (While) -> "ret2", "ret3"
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();

      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) {

import java.util.Set;

/**
 * Collects all registered processors' supported options.
 *
 * <p>This is a workaround for https://bugs.openjdk.java.net/browse/JDK-8162455, which
 * will be triggered a lot more during incremental compilations, and can fail builds
 * when combined with {@code -Werror}.
 *
 * <p>This processor needs to be added last to make sure that all other processors
 * have been {@linkplain Processor#init(ProcessingEnvironment) initialized} when

# 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

// license that can be found in the LICENSE file.

#include "../../../../../runtime/textflag.h"

TEXT asmtest(SB),DUPOK|NOSPLIT,$0
start:
	// Unprivileged ISA

	// 2.4: Integer Computational Instructions

	ADDI	$2047, X5				// 9382f27f
	ADDI	$-2048, X5				// 93820280
	ADDI	$2048, X5				// 9382024093820240
	ADDI	$-2049, X5				// 938202c09382f2bf
	ADDI	$4094, X5				// 9382f27f9382f27f