// done in parallel.
  //
  // This graph is:
  // (Const0, Const0) -> MatMul0
  // (Const1, Const1) -> MatMul1
  // (MatMul0, MatMul1) -> MatMulCombined
  //
  // Device0: [Const0, Const0, MatMul0]
  // Device1: [Const1, Const1, MatMul1, MatMulCombined]
  //
  // Cluster0: [Const0, Const0, MatMul0]
  // Cluster1: [Const1, Const1, MatMul1]
  // Cluster2: [MatMulCombined]

namespace mlir {
namespace TFL {
namespace tac {

// Cost attribute string on the TFL dialect.
constexpr char kCost[] = "tac.cost";

inline void UpdateCost(Operation* op, float cost, OpBuilder* builder) {
  op->setAttr(kCost, builder->getF32FloatAttr(cost));
}

// Get the cost annotated with kCost.
inline bool GetCostOnOp(Operation* op, float* cost) {
  auto cost_type = op->getAttrOfType<FloatAttr>(kCost);

// RUN: tac-opt-all-backends -tfl-compute-cost %s -split-input-file -verify-diagnostics | FileCheck %s

// 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 (pkg *Package) GoVersion() string { return pkg.goVersion }

// Scope returns the (complete or incomplete) package scope
// holding the objects declared at package level (TypeNames,
// Consts, Vars, and Funcs).
// For a nil pkg receiver, Scope returns the Universe scope.
func (pkg *Package) Scope() *Scope {
	if pkg != nil {
		return pkg.scope
	}
	return Universe
}

)

func (Conflict) Conflict() {}

func GFunc[T any]() {}

type GType[T any] int

func (GType[T]) M() {}
`
	const test = `
package p_test

func ExampleConst1() {} // invalid - no support for consts and vars
func ExampleVar1()   {} // invalid - no support for consts and vars

func Example()               {}
func Example_()              {} // invalid - suffix must start with a lower-case letter

// RUN: tac-opt-all-backends -tfl-get-op-cost %s -split-input-file -verify-diagnostics | FileCheck %s

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>

// COM: CHECK-DAG: %[[CONST:.*]] = stablehlo.constant dense<{{.*}}> : tensor<1x1x2x8xf32>
// COM: CHECK: return %[[CONST]] : tensor<1x1x2x8xf32>

func.func @fused_batchnorm_no_training_arg_input(%arg_0: tensor<1x1x2x8xf32>) -> (tensor<1x1x2x8xf32>) {
  %cst_0 = "tf.Const"() {value = dense<[0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1, 0.2]> : tensor<8xf32>} : () -> tensor<8xf32>

absl::StatusOr<Node*> ReplaceFunctionCallWithPartitionedCall(
    const GraphOptimizationPassOptions& options,
    const FunctionLibraryDefinition& flib_def, Node* n, Graph* g,
    const NameAttrList& func, const Scope& root) {
  string config_string = options.session_options->config.SerializeAsString();

  int input_count = absl::c_count_if(
      n->in_edges(), [](const Edge* e) { return !e->IsControlEdge(); });

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...

)

// numeric constant conversions are in const1.src.

func string_conversions() {
	const A = string(65)
	assert(A == "A")
	const E = string(-1)
	assert(E == "\uFFFD")
	assert(E == string(1234567890))

	type myint int
	assert(A == string(myint(65)))

	type mystring string
	const _ mystring = mystring("foo")

	const _ = string(true /* ERROR "cannot convert" */ )
	const _ = string(1.2 /* ERROR "cannot convert" */ )