}

bool IsInSubgraph(const string& func_id) { return !func_id.empty(); }

Status Encapsulator::CopySubgraphNodes(
    absl::flat_hash_map<const Node*, Node*>* node_images) {
  for (Node* node : graph_in_->op_nodes()) {
    string func_id;
    TF_RETURN_IF_ERROR(GetFunctionNameAttr(node, &func_id));
    if (!IsInSubgraph(func_id)) continue;

    Subgraph& subgraph = subgraphs_[func_id];

  return IsReplicatedGraph(module);
}

bool HasTPUPartitionedCallOpInModule(mlir::ModuleOp module) {
  bool has_tpu_partitioned_call = false;
  for (auto func_op : module.getOps<mlir::func::FuncOp>()) {
    func_op->walk([&](mlir::TF::TPUPartitionedCallOp op) {
      has_tpu_partitioned_call = true;
    });
    if (has_tpu_partitioned_call) break;
  }
  return has_tpu_partitioned_call;
}

        given:
        sourceFile << """
            #define FUNC2(X) X
            #define FUNC1(X) FUNC2
            #define HEADER(X) X
            #include HEADER(FUNC1 (~) ("hello.h")) // replaced by FUNC1(~)("hello.h") then FUNC2("hello.h")
        """

        expect:
        resolve() == [header]
    }

      llvm_unreachable("no implementation.");
      return failure();
    }
  }
  return success();
}

LogicalResult QuantizeContext::Finalize() {
  MLIRContext *context = func_.getContext();
  func_.walk([&](quantfork::QuantizeRegionOp op) {
    llvm::SmallVector<Attribute, 4> input_specs;
    auto original_input_specs = op.getInputSpecs().getValue();
    for (int i = 0, e = op.getNumOperands(); i != e; ++i) {

                TF_Status* status) {
  TFE_Op* func_op = TFE_NewOp(ctx, func.name().data(), status);
  for (const auto& attr : func.attr()) {
    if (!status->status.ok()) return nullptr;
    SetOpAttrValueScalar(ctx, func_op, attr.second, attr.first.data(), status);
    if (!status->status.ok()) return nullptr;
  }
  return func_op;
}
}  // namespace

                              const bool infer_tensor_range,
                              const bool legacy_float_scale = false,
                              const bool is_qdq_conversion = false)
      : fn_(func_op),
        builder_(func_op.getBody()),
        is_signed_(is_signed),
        bit_width_(bit_width),
        disable_per_channel_(disable_per_channel),
        op_quant_spec_getter_(op_quant_spec_getter),

		p = alloc(3) // ERROR "inlining call to alloc"
	}() // ERROR "inlining call to f1.func1" "inlining call to alloc" "moved to heap: x"

	f = func() { // ERROR "func literal escapes to heap" "can inline f1.func2"
		p = alloc(3) // ERROR "inlining call to alloc" "moved to heap: x"
	}
	f()
}

func f2() {} // ERROR "can inline f2"

// No inline for recover; panic now allowed to inline.

// to set attributes specific to this pass.
void AddAttrs(OpsAdded& ops_added, OpBuilder& builder, int func_count) {
  func::FuncOp& added_func_op = ops_added.func_op;
  func::CallOp& added_call_op = ops_added.call_op;
  StringAttr interface_name =
      builder.getStringAttr(absl::StrCat("func_", func_count));

  added_func_op->setAttr(kInterfaceNameAttr, interface_name);
  added_call_op->setAttr(kInterfaceNameAttr, interface_name);

#define FUNC1(a, b, c, d, e) \
	MOVL	d, DI; \
	XORL	c, DI; \
	ANDL	b, DI; \
	XORL	d, DI

#define FUNC2(a, b, c, d, e) \
	MOVL	b, DI; \
	XORL	c, DI; \
	XORL	d, DI

#define FUNC3(a, b, c, d, e) \
	MOVL	b, SI; \
	ORL	c, SI; \
	ANDL	d, SI; \
	MOVL	b, DI; \
	ANDL	c, DI; \
	ORL	SI, DI

#define FUNC4 FUNC2

#define MIX(a, b, c, d, e, const) \
	ROLL	$30, b; \
	ADDL	DI, e; \
	MOVL	a, SI; \

      /*function_aliases=*/{{"func1", "alias1"}, {"func2", "alias2"}},
      /*asset_file_defs=*/{});
  ASSERT_THAT(exported_model.function_aliases(), SizeIs(2));
  EXPECT_TRUE(exported_model.function_aliases().contains("func1"));
  EXPECT_THAT(exported_model.function_aliases().at("func1"), StrEq("alias1"));
  EXPECT_TRUE(exported_model.function_aliases().contains("func2"));