// we've already reached the maximum number of ongoing compilations allowed.
  profiler->RegisterExecution(function);
  EXPECT_FALSE(
      profiler->ShouldCompileCluster(function, DeviceCompileMode::kAsync, 0));

  profiler->DecrementOngoingAsyncCompilations();
  // Should allow compilation since we've decremented the number of ongoing
  // compilations.
  EXPECT_TRUE(

import org.gradle.operations.execution.FilePropertyVisitor;

import javax.annotation.Nullable;
import java.util.List;
import java.util.Map;

/**
 * Represents the computation of capturing the before execution state and resolving the caching state for transforms.
 * <p>
 * Must occur as a child of {@link org.gradle.operations.execution.ExecuteWorkBuildOperationType}.
 *
 * @since 8.3
 */

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). */

    // TODO(ycao): Support tensorlist-type output.
    out_desc.is_tensor_list = false;
  }

  // XLA computation always uses Tuple shape.
  *xla_output_shape = xla::ShapeUtil::MakeTupleShape(shapes);
  return absl::OkStatus();
}

// Creates a vector that maps from the parameters of the XLA computation to
// their original argument positions.
// MLIR-based TF-Compiler bridge doesn't have constant analysis yet, thus no

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

package ssa

import (
	"cmd/internal/obj"
	"sort"
)

// A Cache holds reusable compiler state.
// It is intended to be re-used for multiple Func compilations.
type Cache struct {
	// Storage for low-numbered values and blocks.
	values [2000]Value
	blocks [200]Block
	locs   [2000]Location

	// Reusable stackAllocState.

    const XlaCompiler::CompilationResult& result) {
  VLOG(2) << "Compiling to xla::PjRtLoadedExecutable.";

  TF_ASSIGN_OR_RETURN(auto executable,
                      client_->Compile(*result.computation,
                                       GetPjRtCompileOptions(options, result)));

  VLOG(2) << "Compiled PJRT executable " << executable->name()
          << " num_replicas " << executable->num_replicas()

func blockGeneric(dig *digest, p []byte) {
	var w [64]uint32
	h0, h1, h2, h3, h4, h5, h6, h7 := dig.h[0], dig.h[1], dig.h[2], dig.h[3], dig.h[4], dig.h[5], dig.h[6], dig.h[7]
	for len(p) >= chunk {
		// Can interlace the computation of w with the
		// rounds below if needed for speed.
		for i := 0; i < 16; i++ {
			j := i * 4
			w[i] = uint32(p[j])<<24 | uint32(p[j+1])<<16 | uint32(p[j+2])<<8 | uint32(p[j+3])
		}
		for i := 16; i < 64; i++ {

      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

    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();