class QuantizeConstWeights : public OpRewritePattern<TF::ConstOp> {
 public:
  explicit QuantizeConstWeights(
      MLIRContext* context,
      const tensorflow::quantization::QuantizationOptions& quantization_options)
      : OpRewritePattern<TF::ConstOp>(context),
        quant_options_(quantization_options) {}

  LogicalResult matchAndRewrite(TF::ConstOp op,

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

// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 4, 4 ],
// CHECK-NEXT:       name: "Const",
// CHECK-NEXT:       quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       is_variable: true,
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 4, 4 ],
// CHECK-NEXT:       name: "Const1",
// CHECK-NEXT:       quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:       },

			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

  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

func.func @body(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>) {
  %cst = arith.constant dense<1> : tensor<i32> loc("Const")

		return cstring(sh.Name[:]), nil
	}
	i, err := strconv.Atoi(cstring(sh.Name[1:]))
	if err != nil {
		return "", err
	}
	return st.String(uint32(i))
}

// TODO(brainman): copy all IMAGE_REL_* consts from ldpe.go here

// Reloc represents a PE COFF relocation.
// Each section contains its own relocation list.
type Reloc struct {
	VirtualAddress   uint32
	SymbolTableIndex uint32
	Type             uint16
}

  // operations specified (0).
  void DefineT(int num_opers, const std::vector<TF_Operation*>& opers,
               const std::vector<TF_Output>& inputs,
               const std::vector<TF_Output>& outputs,
               const std::vector<string>& output_names,
               bool expect_failure = false) {
    ASSERT_EQ(func_, nullptr);
    const char** output_names_ptr = ToArray(output_names);

func init() {
	rootCmd.Flags().StringVarP(&input, "inputFile", "i", "features.yaml", "the YAML file to use as input")
	rootCmd.Flags().StringVarP(&output, "outputFile", "o", "features.gen.go", "output Go file with labels as string consts")
}

// Parses a map in the yaml file
func readMap(m map[any]any, path []string) []string {
	var labels []string
	for k, v := range m {

StatusOrOptional<SliceInputs> GetSliceInputs(Node* slice) {
  const int kSliceInputIndex = 0;
  const int kSliceBeginIndex = 1;
  const int kSliceSizeIndex = 2;

  const Edge* slice_input_edge;
  TF_RETURN_IF_ERROR(slice->input_edge(kSliceInputIndex, &slice_input_edge));
  const Edge* slice_size_edge;
  TF_RETURN_IF_ERROR(slice->input_edge(kSliceSizeIndex, &slice_size_edge));
  const Edge* slice_begin_edge;