using OptionalOpShardingVector =
    llvm::SmallVector<std::optional<OpShardingVariant>, 8>;
using llvm::StringRef;
using mlir::Block;
using mlir::BlockArgument;
using mlir::BoolAttr;
using mlir::Builder;
using mlir::IntegerAttr;
using mlir::LogicalResult;
using mlir::ModuleOp;
using mlir::Operation;
using mlir::OpOperand;
using mlir::OpResult;
using mlir::RankedTensorType;

	Name    [1024]int8
	_       [3]byte
}

type Attrlist struct {
	Bitmapcount uint16
	Reserved    uint16
	Commonattr  uint32
	Volattr     uint32
	Dirattr     uint32
	Fileattr    uint32
	Forkattr    uint32
}

const (
	PathMax = 0x400
)

type RawSockaddrInet4 struct {
	Len    uint8
	Family uint8
	Port   uint16
	Addr   [4]byte /* in_addr */
	Zero   [8]int8
}

//===----------------------------------------------------------------------===//

def ReadVariableOfCast : Pat<
  (TF_ReadVariableOp:$src (TF_CastOp:$output $x, BoolAttr:$Truncate)),
  (TF_ReadVariableOp:$dest $x), [(HasOnlyReadVariableOpUsers $output)],
  [(CopyAttrs $src, $dest)]>;

//===----------------------------------------------------------------------===//

	Name    [1024]int8
	_       [3]byte
}

type Attrlist struct {
	Bitmapcount uint16
	Reserved    uint16
	Commonattr  uint32
	Volattr     uint32
	Dirattr     uint32
	Fileattr    uint32
	Forkattr    uint32
}

const (
	PathMax = 0x400
)

type RawSockaddrInet4 struct {
	Len    uint8
	Family uint8
	Port   uint16
	Addr   [4]byte /* in_addr */
	Zero   [8]int8
}

    The `tf.IfrtCall` kernel uses the output $array_key.
    Other ops executed by TFRT may make use of $tensor_future.
  }];

  let arguments = (ins
    TFTensorType:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TFTensorType:$array_key,
    MlrtFutureType: $tensor_future
  );
}

def IfrtRestoreVariableOp: TensorflowMlrt_Op<"ifrt_restore_variable", []> {

                          lhs_dot_dimensions_info.FlattenedOutDimensionSize()},
                      llvm::ArrayRef<int64_t>{
                          rhs_dot_dimensions_info.FlattenedOutDimensionSize()});
  BoolAttr false_attr = rewriter.getBoolAttr(false);
  auto matmul = rewriter.create<TFL::BatchMatMulOp>(
      loc, RankedTensorType::get(matmul_shape, result_type.getElementType()),

                                      ImplicitLocOpBuilder& builder,
                                      bool is_lhs) {
  auto operand_type = mlir::cast<ShapedType>(operand.getType());
  BoolAttr true_attr = builder.getBoolAttr(true);
  auto operand_shape = builder.create<TF::ShapeOp>(operand, true_attr);
  const int64_t operand_rank = operand_type.getRank();

           << "]";

  return success();
}

bool VariableIsInitialized(TF::VarHandleOp var_handle_op) {
  auto is_variable_initialized =
      var_handle_op->getAttrOfType<BoolAttr>("_is_initialized");
  // Assume variable is initialized if attribute is not set.
  // There are paths that doesn't mark the variables. All variables
  // that doesn't have the attribute will be promoted.

  NativeCodeCall<"GetI64ElementsAttr({" # value # "}, &$_builder)">;

class GetF32Scalar<int value> :
  NativeCodeCall<"GetF32Scalar(&$_builder, " # value # ")">;

def TrueBoolAttr : AttrConstraint<CPred<"$_self.cast<::mlir::BoolAttr>().getValue()">>;

def CreateTFShapeOp : NativeCodeCall<
    "$_builder.create<TF::ShapeOp>($0.getLoc(), $1, $2)">;

def IsI32 : NativeCodeCall<

    return true;
  }

  OpBuilder builder(func);
  bool is_signed = quant_specs_.IsSignedInferenceType();
  IntegerAttr num_bits =
      builder.getI32IntegerAttr(quant_specs_.GetQuantizationTypeWidth());
  BoolAttr narrow_range = builder.getBoolAttr(false);

  auto add_quantize_op = [&](Location loc, Type input_type, Block* block,
                             Block::iterator insertion_point, Value arg,