a[i] = bits.RotateLeft32(a[i], r) & 0xFFF00FFF
	i++
	// ppc64x: "RLWNM\t[$]4, R[0-9]+, [$]20, [$]11, R[0-9]+"
	a[i] = bits.RotateLeft32(a[i], 4) & 0xFFF00FFF
	i++
}

// combined arithmetic and rotate on arm64
func checkArithmeticWithRotate(a *[1000]uint64) {
	// arm64: "AND\tR[0-9]+@>51, R[0-9]+, R[0-9]+"
	a[2] = a[1] & bits.RotateLeft64(a[0], 13)
	// arm64: "ORR\tR[0-9]+@>51, R[0-9]+, R[0-9]+"

import com.google.common.annotations.J2ktIncompatible;
import java.math.BigInteger;
import javax.annotation.CheckForNull;

/**
 * A wrapper class for unsigned {@code int} values, supporting arithmetic operations.
 *
 * <p>In some cases, when speed is more important than code readability, it may be faster simply to
 * treat primitive {@code int} values as unsigned, using the methods from {@link UnsignedInts}.
 *

import com.google.common.annotations.J2ktIncompatible;
import java.math.BigInteger;
import javax.annotation.CheckForNull;

/**
 * A wrapper class for unsigned {@code int} values, supporting arithmetic operations.
 *
 * <p>In some cases, when speed is more important than code readability, it may be faster simply to
 * treat primitive {@code int} values as unsigned, using the methods from {@link UnsignedInts}.
 *

// input and value tensors. It also accepts the copy callback provided by
// pluggable vendor to do the copying of the tensors and the update callback to
// apply the arithmetic operation. The caller takes ownership of the `source`,
// `dest`, `tensor` and `value` tensors and is responsible for freeing them with
// TF_DeleteTensor.
TF_CAPI_EXPORT extern void TF_AssignUpdateVariable(

  let constructor = "mlir::quant::stablehlo::CreateQuantizeWeightPass()";
}

def UnfuseMhloBatchNormPass : Pass<"stablehlo-unfuse-mhlo-batch-norm", "mlir::func::FuncOp"> {
  let summary = "Unfuses batch normalization into arithmetic ops.";
}

def LiftQuantizableSpotsAsFunctionsPass : Pass<"stablehlo-lift-quantizable-spots-as-functions", "mlir::ModuleOp"> {
  let summary = "Replace quantization candidates with composite functions into the module.";

  let constructor = "CreateIdentifyDilatedConvPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

def GetArithmeticCountPass : Pass<"tfl-get-arithmetic-count", "mlir::func::FuncOp"> {
  let summary = "Calculate arithmetic count for tfl operations.";
  let constructor = "CreateGetArithmeticCountPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

		// Otherwise use the raw address, so that things like relative
		// jumps show up as JMP 0x123 instead of JMP f+10(SB).
		// It is usually easier to search for 0x123 than to do the mental
		// arithmetic to find f+10.
		addr := pc + uint64(inst.Len) + uint64(a)
		if s, base := symname(addr); s != "" && addr == base {
			return fmt.Sprintf("%s(SB)", s)
		}
		return fmt.Sprintf("%#x", addr)

	case Imm:

 public:
  StringRef getArgument() const final { return "unfuse-mhlo-batch-norm-pass"; }
  StringRef getDescription() const final {
    return "Unfuses MHLO batch norm inference op into arithmetic ops";
  }
  void getDependentDialects(::mlir::DialectRegistry &registry) const override {
    // TODO(b/299514833): Remove TensorFlowDialect usage.
    registry.insert<shape::ShapeDialect, mlir::TF::TensorFlowDialect>();

	_ = unsafe.Sizeof(func() { type _ [iota /* ERROR "iota outside constant decl" */ ]byte })
	_ = unsafe.Sizeof(func() { func() int { return iota /* ERROR "iota outside constant decl" */ }() })
)

// constant arithmetic precision and rounding must lead to expected (integer) results
var _ = []int64{
	0.0005 * 1e9,
	0.001 * 1e9,
	0.005 * 1e9,
	0.01 * 1e9,
	0.05 * 1e9,
	0.1 * 1e9,
	0.5 * 1e9,
	1 * 1e9,
	5 * 1e9,

  /// construct an Initializer. Initializer stores the value it got constructed
  /// with in a Tensor object.
  struct Initializer {
    /// Construct from a scalar value of an arithmetic type or a type that can
    /// be converted to a string (eg. a string literal).
    template <typename T, typename = typename std::enable_if<
                              std::is_arithmetic<T>::value ||