),
)

tf_cc_binary(
    name = "bef_executor",
    testonly = True,
    visibility = [":friends"],
    deps = [
        "@tf_runtime//:dtype",
        "@tf_runtime//:simple_tracing_sink",
        "@tf_runtime//tools:bef_executor_expensive_kernels",
        "@tf_runtime//tools:bef_executor_lib",
        "@tf_runtime//tools:bef_executor_lightweight_kernels",

ElementsAttr GetTensorValueAsElementsAttr(
    TF::VarHandleOp var_handle_op, const tensorflow::Tensor& resource_tensor,
    const tensorflow::DeviceMgr* mgr, OpBuilder builder) {
  if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {
    return GetTensorValueAsElementsAttr(resource_tensor, builder);
  }

  auto handle = resource_tensor.scalar<tensorflow::ResourceHandle>()();

        llvm::dyn_cast<TF::ConstOp>(fold_results[0].getDefiningOp()).getValue(),
        &tensor));
    xla::Shape xla_shape;
    TF_RETURN_IF_ERROR(tensorflow::TensorShapeToXLAShape(
        tensor.dtype(), tensor.shape(), &xla_shape));
    xla::PjRtClient::HostBufferSemantics host_buffer_semantics =
        xla::PjRtClient::HostBufferSemantics::kImmutableUntilTransferCompletes;
    TF_ASSIGN_OR_RETURN(
        auto buffer,

  // sharding.
  // The split sharding is:
  //   type: OTHER
  //   tile_assignment_dimensions: 2
  //   tile_assignment_dimensions: 1
  //   tile_assignment_devices: 0
  //   tile_assignment_devices: 1
  // Serialized string:
  //   "\08\03\1A\02\02\01\22\02\00\01"
  // The manual sharding is:
  //   type: MANUAL
  // Serialized string:
  //   "\08\04"

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

// Runtime type representation.

package runtime

import (
	"internal/abi"
	"unsafe"
)

type nameOff = abi.NameOff
type typeOff = abi.TypeOff
type textOff = abi.TextOff

type _type = abi.Type

// rtype is a wrapper that allows us to define additional methods.
type rtype struct {
	*abi.Type // embedding is okay here (unlike reflect) because none of this is public

	return s[i+1:]
}

func toRType(t *abi.Type) rtype {
	return rtype{t}
}

func elem(t *abi.Type) *abi.Type {
	et := t.Elem()
	if et != nil {
		return et
	}
	panic("reflect: Elem of invalid type " + toRType(t).String())
}

func (t rtype) Elem() Type {
	return toType(elem(t.common()))
}

func (t rtype) In(i int) Type {
	tt := t.Type.FuncType()
	if tt == nil {

}

// Imethod represents a method on an interface type
type Imethod struct {
	Name NameOff // name of method
	Typ  TypeOff // .(*FuncType) underneath
}

// ArrayType represents a fixed array type.
type ArrayType struct {
	Type
	Elem  *Type // array element type
	Slice *Type // slice type
	Len   uintptr
}

// Len returns the length of t if t is an array type, otherwise 0
func (t *Type) Len() int {

)

// userTypeInfo stores the information associated with a type the user has handed
// to the package. It's computed once and stored in a map keyed by reflection
// type.
type userTypeInfo struct {
	user        reflect.Type // the type the user handed us
	base        reflect.Type // the base type after all indirections
	indir       int          // number of indirections to reach the base type
	externalEnc int          // xGob, xBinary, or xText

	stringEface any = stringInterfacePtr("")
	sliceEface  any = sliceInterfacePtr(nil)

	uint16Type *_type = efaceOf(&uint16Eface)._type
	uint32Type *_type = efaceOf(&uint32Eface)._type
	uint64Type *_type = efaceOf(&uint64Eface)._type
	stringType *_type = efaceOf(&stringEface)._type
	sliceType  *_type = efaceOf(&sliceEface)._type
)

// The conv and assert functions below do very similar things.

LogicalResult ConstantFoldMul(mhlo::MulOp op, PatternRewriter &rewriter) {
  ShapedType type = mlir::dyn_cast<ShapedType>(op.getType());
  Type etype = type.getElementType();
  Attribute result = {};
  if (mlir::isa<FloatType>(etype)) {
    result =
        BinaryFolder<mhlo::MulOp, FloatType, APFloat, std::multiplies<APFloat>>(
            &op);
  } else if (mlir::isa<IntegerType>(etype)) {
    result =