// CHECK-SAME: ([[ARG0:%.*]]: tensor<i32>, [[ARG1:%.*]]: tensor<f32>, [[ARG2:%.*]]: tensor<*x!tf_type.resource<tensor<i32>>>)
func.func @write_only_resource(%arg0: tensor<i32>, %arg1: tensor<f32>, %arg2: tensor<*x!tf_type.resource<tensor<i32>>>) {
  // CHECK-NEXT: [[READ:%.*]] = "tf.ReadVariableOp"([[ARG2]])
  // CHECK-NEXT: [[CLUSTER:%.*]]:2 = "tf_device.cluster_func"([[ARG0]], [[ARG1]], [[READ]])

  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "GPU"} : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<10x10x10xf32>
  func.return %0 : tensor<10x10x10xf32>
}

// -----

// CHECK: tac.cost = 2.000000e+03

// -----

func.func @test_fuse_dynamic_dimension_ops(%arg0: tensor<*xi32>, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*xi32>, %arg3: tensor<*xi32>, %arg4: tensor<*xi32>, %arg5: tensor<?xi64>, %arg6: tensor<?xi64>, %arg7: tensor<?xi64>) -> tensor<*xi32> {
  %0 = "tf.ReadVariableOp"(%arg1) {__op_key = 0: i32, device = "/CPU:0"} : (tensor<*x!tf_type.resource>) -> tensor<*xi32>

}

// -----

func.func @testConv2DCPU(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x32x32x16xf32> {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>):
  // CHECK: tac.cost = 0x4D5C0000

module {
  func.func @main(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>, %arg2: tensor<100xf32>, %arg3: tensor<100xf32>) -> tensor<2x100xf32> {
    %0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_0"} : (tensor<100xf32>, tensor<100xf32>, tensor<100xf32>) -> tensor<100xf32>

// CHECK:         tf.GlobalIterId
func.func @globaliterid_not_hoisted(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.OpA"() {is_stateless = true} : () -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.GlobalIterId"() : () -> tensor<i64>

// CHECK-LABEL: listSetItem
func.func @listSetItem(%arg0: tensor<!tf_type.variant<tensor<*xi32>>>, %arg1: tensor<i32>, %arg2: tensor<*xi32>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %0 = "tf.TensorListSetItem"(%arg0, %arg1, %arg2) : (tensor<!tf_type.variant<tensor<*xi32>>>, tensor<i32>, tensor<*xi32>) -> tensor<!tf_type.variant<tensor<*xi32>>>

		{name: "ADDF", argLength: 2, reg: fp21, asm: "ADDF", commutative: true}, // arg0 + arg1
		{name: "ADDD", argLength: 2, reg: fp21, asm: "ADDD", commutative: true}, // arg0 + arg1
		{name: "SUBF", argLength: 2, reg: fp21, asm: "SUBF"},                    // arg0 - arg1

  // CHECK-DAG: ml_program.global public @vars.Variable_1([]) : tensor<!tf_type.string>
  func.func @lowers_string_ops(%arg0: tensor<128xi32>, %arg1: tensor<128xi32>, %arg2: tensor<128x1xi32>, %arg3: tensor<128x90xi32>, %arg4: tensor<128x90xi32>, %arg5: tensor<128x90xi32>, %arg6: tensor<128x90x64xf32>, %arg7: tensor<128x90x64xf32>) -> tensor<!tf_type.string> {
    // CHECK: %[[v0:.*]] = ml_program.global_load @vars.Variable_1 : tensor<!tf_type.string>

		{name: "SUBD", argLength: 2, reg: fp21, asm: "SUBD"},                    // arg0 - arg1
		{name: "MULF", argLength: 2, reg: fp21, asm: "MULF", commutative: true}, // arg0 * arg1
		{name: "MULD", argLength: 2, reg: fp21, asm: "MULD", commutative: true}, // arg0 * arg1
		{name: "DIVF", argLength: 2, reg: fp21, asm: "DIVF"},                    // arg0 / arg1