c := 5
				func(z int) { // ERROR "can inline main.func28.1.1" "can inline main.func28.main.func28.1.2" "can inline main.main.func28.func35.1" "can inline main.main.func28.main.main.func28.func35.func37"
					a = a * x
					b = b * y
					c = c * z
				}(10) // ERROR "inlining call to main.func28.1.1"
				return a + c

func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  // CHECK: tac.cost = 7.864320e+05
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>

// 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...
//   %1 = tfl.const...
class FoldConstantsToSubgraphPass

Rob Pike <******@****.***> 1329622133 +1100

// CHECK: tac.cost = 7.864320e+05
func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>
  func.return %0 : tensor<256x32x32x3xf32>
}

	go func() { // func9
		cv.L.Lock()
		cv.Wait()
		cv.L.Unlock()
	}()
	ln, err := net.Listen("tcp", "127.0.0.1:0")
	if err != nil {
		log.Fatalf("failed to listen: %v", err)
	}
	go func() { // func10
		c, err := ln.Accept()
		if err != nil {
			log.Printf("failed to accept: %v", err)
			return
		}
		c.Close()
	}()
	rp, wp, err := os.Pipe()
	if err != nil {

// exist.
// TODO(b/284222084): Move executor dialect utilities to a new library.
GraphOp GetGraphOpFromFuncOp(func::FuncOp func_op) {
  if (func_op->getNumRegions() == 0 || func_op.getBody().empty()) return {};

  auto graph_op_range = func_op.front().without_terminator();
  if (llvm::hasSingleElement(graph_op_range)) {
    // The pass runs on a valid tf_executor dialect, so the op should be the
    // GraphOp.

static bool FunctionContainsOnlyNoSideEffectOpOrTensorArrayOp(
    mlir::func::FuncOp func_op) {
  for (mlir::Operation& op : func_op.front()) {
    if (!mlir::isMemoryEffectFree(&op) && !IsTensorArrayOp(&op)) return false;
  }

  return true;
}

TensorArraySideEffectAnalysis::TensorArraySideEffectAnalysis(
    mlir::ModuleOp module) {
  for (auto func_op : module.getOps<mlir::func::FuncOp>()) {

    SymbolTable symbol_table(call_op->getParentOfType<ModuleOp>());
    auto func_op =
        dyn_cast_or_null<func::FuncOp>(symbol_table.lookup(func_name));
    if (!func_op) return failure();
    // The quantized fusion should have requantize and return ops at the end.
    auto return_op = dyn_cast_or_null<func::ReturnOp>(
        func_op.getRegion().getBlocks().front().getTerminator());
    if (!return_op) return failure();

  if (call_op_has_platform_index_arg) {
    arg_mapper.map(func_op.getArgument(0),
                   builder.create<mhlo::ConstantOp>(
                       func_op.getLoc(), builder.getI16IntegerAttr(0)));
  }
  for (auto [func_arg, operand] : llvm::zip_equal(
           func_op.getArguments().take_back(call_op.getNumOperands()),
           call_op.getOperands())) {