// CHECK-DAG: %[[CONST1:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>
  // CHECK-DAG: %[[CONST2:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}>
  // CHECK: %[[TRANS1:.*]] = "tf.Transpose"(%arg0, %[[CONST1]]) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>

namespace tac {
namespace {

// This pass is used to fold tfl.const ops to each subgraph (func::FuncOp):
// See the example below:
//
// In main:
// %0 = tfl.const...
// %1 = tfl.const...
// %2 = call func_1(..., %0,...)
// %3 = call func_2(..., %0, ..., %1...)
// ...
//
// Then those consts will be copied into each function and replace their usage.
// func_1:
//   %0 = tfl.const...
// func_2:
//   %0 = tfl.const...

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")
  %1 = "tfl.sub" (%arg0, %0) {fused_activation_function = "RELU6"} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32> loc("sub")
  %2 = "arith.constant" () {value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("Const2")

  const auto filename_op_type = RankedTensorType::get(
      /*shape=*/{}, /*elementType=*/builder.getType<TF::StringType>());
  const auto file_prefix_attr = builder.getStringAttr(kTfFilePrefix);
  const auto arg_attrs = builder.getDictionaryAttr({builder.getNamedAttr(
      kTfSavedModelIndexPathAttr, builder.getArrayAttr({file_prefix_attr}))});

  const int insert_idx = func_op.getNumArguments();

DATA consts<>+0x48(SB)/8, $0x0102030005060704
DATA consts<>+0x50(SB)/8, $0x6170786561707865
DATA consts<>+0x58(SB)/8, $0x6170786561707865
DATA consts<>+0x60(SB)/8, $0x3320646e3320646e
DATA consts<>+0x68(SB)/8, $0x3320646e3320646e
DATA consts<>+0x70(SB)/8, $0x79622d3279622d32
DATA consts<>+0x78(SB)/8, $0x79622d3279622d32
DATA consts<>+0x80(SB)/8, $0x6b2065746b206574
DATA consts<>+0x88(SB)/8, $0x6b2065746b206574

	constructor := make(map[*doc.Func]bool)
	for _, typ := range docPkg.Types {
		docPkg.Consts = append(docPkg.Consts, typ.Consts...)
		docPkg.Vars = append(docPkg.Vars, typ.Vars...)
		docPkg.Funcs = append(docPkg.Funcs, typ.Funcs...)
		if isExported(typ.Name) {
			for _, value := range typ.Consts {
				typedValue[value] = true
			}
			for _, value := range typ.Vars {
				typedValue[value] = true
			}

  explicit QuantizeWeightsPass(
      const tensorflow::quantization::QuantizationOptions& quant_options)
      : test_mode_(false), quant_options_(quant_options) {}

  QuantizeWeightsPass(const QuantizeWeightsPass& other) {
    test_mode_ = other.test_mode_;
    quant_options_ = other.quant_options_;
    initializeForTest();
  }

  StringRef getArgument() const final {

    %cst_1 = "tf.Const"() {value = dense<-5.000000e-01> : tensor<f32>} : () -> tensor<f32>
    %cst_2 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %cst_3 = "tf.Const"() {value = dense<0.0274509806> : tensor<f32>} : () -> tensor<f32>
    %cst_4 = "tf.Const"() {value = dense<-55> : tensor<i32>} : () -> tensor<i32>
    %cst_5 = "tf.Const"() {value = dense<-1.280000e+02> : tensor<f32>} : () -> tensor<f32>

			if *verbose {
				fmt.Fprintln(os.Stderr, err)
			}
			return nil
		}
		for _, pkg := range pkgs {
			d := doc.New(pkg, path, doc.Mode(0))
			list := appendHeadings(nil, d.Doc)
			for _, d := range d.Consts {
				list = appendHeadings(list, d.Doc)
			}
			for _, d := range d.Types {
				list = appendHeadings(list, d.Doc)
			}
			for _, d := range d.Vars {
				list = appendHeadings(list, d.Doc)
			}

// Creates a pass that creates a RestoreV2 op in the initializer function with
// type "restore_op" that initializes variables from the checkpoint. It finds
// tf.AssignVariableOp(tf.VarHandleOp, tf.Const) patterns in the initializer
// function and replaces tf.Consts with the results of RestoreV2.
std::unique_ptr<OperationPass<ModuleOp>> CreateInsertRestoreOpPass();

// Creates a pass that creates a new function that wraps the newly created