}
func (f *Func) ConstInt32(t *types.Type, c int32) *Value {
	return f.constVal(OpConst32, t, int64(c), true)
}
func (f *Func) ConstInt64(t *types.Type, c int64) *Value {
	return f.constVal(OpConst64, t, c, true)
}
func (f *Func) ConstFloat32(t *types.Type, c float64) *Value {
	return f.constVal(OpConst32F, t, int64(math.Float64bits(float64(float32(c)))), true)
}
func (f *Func) ConstFloat64(t *types.Type, c float64) *Value {

// (Call typecheck.Func instead.)
func tcFunc(n *ir.Func) {
	if base.EnableTrace && base.Flag.LowerT {
		defer tracePrint("tcFunc", n)(nil)
	}

	if name := n.Nname; name.Typecheck() == 0 {
		base.AssertfAt(name.Type() != nil, n.Pos(), "missing type: %v", name)
		name.SetTypecheck(1)
	}
}

// tcCall typechecks an OCALL node.
func tcCall(n *ir.CallExpr, top int) ir.Node {

#define TENSORFLOW_COMPILER_MLIR_QUANTIZATION_COMMON_FUNC_H_

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project

namespace mlir::quant {

// Returns a public `func::FuncOp` in `module_op` whose name matches either
// `main` or `serving_default`. If `func::FuncOps` with both names exist, the
// function with name "main" takes precedence. Returns null if no such a

	FuncFlagAsm
)

// A FuncID identifies particular functions that need to be treated
// specially by the runtime.
// Note that in some situations involving plugins, there may be multiple
// copies of a particular special runtime function.
type FuncID uint8

const (
	// If you add a FuncID, you probably also want to add an entry to the map in
	// ../../cmd/internal/objabi/funcid.go

  for (auto func : module.getOps<func::FuncOp>()) {
    llvm::BitVector args_to_erase(func.getNumArguments());
    OpBuilder builder(func.getBody());

    for (int i = 0, e = func.getNumArguments(); i < e; ++i) {
      SmallVector<TF::InitializeTableFromTextFileV2Op, 4>
          init_table_from_text_file_ops_to_erase;
      auto asset = LookupBoundInputOfType<AssetOp>(func, i, symbol_table);

// CHECK-LABEL: func @func_attr
// CHECK-SAME: tf._implements = #tf_type.func<@symbol_a, {attr0 = 1 : i32, attr1 = "random"}>
func.func @func_attr() attributes {tf._implements = #tf_type.func<@symbol_a, {attr0 = 1 : i32, attr1 = "random"}>} {
  func.return
}

// CHECK-LABEL: func @nested_func_attr

 private:
  TFSignatureDefFunction(std::unique_ptr<FlatTensorFunction> func,
                         SignatureDefFunctionMetadata metadata);

  TFSignatureDefFunction(const TFSignatureDefFunction&) = delete;
  TFSignatureDefFunction& operator=(const TFSignatureDefFunction&) = delete;

  std::unique_ptr<FlatTensorFunction> func_;
  SignatureDefFunctionMetadata metadata_;
};

}  // namespace tensorflow

  void runOnOperation() override;
};

void CanonicalizeCompileAndReplicateAttributesPass::runOnOperation() {
  func::FuncOp func_op = getOperation();
  ModuleOp module_op = func_op->getParentOfType<ModuleOp>();
  mlir::OpBuilder builder(module_op.getContext());

  auto walk_result = func_op->walk([&](mlir::Operation* op) {
    // Convert `_tpu_replicate`.
    if (op->hasAttr(TF::kTpuReplicateAttr)) {

               tensor_type,
               SmallVector<StringRef>(str_values.begin(), str_values.end())));
}

// Creates a new argument for `func_op` that accepts a string tensor containing
// the checkpoint file's prefix.
BlockArgument InsertFilePrefixArgument(func::FuncOp func_op,
                                       OpBuilder& builder) {
  const auto filename_op_type = RankedTensorType::get(

 public:
  void runOnOperation() override;
};

void InferenceMetricsPass::runOnOperation() {
  bool has_tpu_partitioned_call = false;
  ModuleOp module = getOperation();

  for (auto func_op : module.getOps<func::FuncOp>()) {
    func_op->walk(
        [&](TF::TPUPartitionedCallOp op) { has_tpu_partitioned_call = true; });

    if (has_tpu_partitioned_call) break;
  }

  std::string has_tpu_partitioned_call_str =