// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [  ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {
// CHECK-NEXT:      shape: [ 5 ],
// CHECK-NEXT:      buffer: 1,
// CHECK-NEXT:      name: "StatefulPartitionedCall:1",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:      },
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    } ],
// CHECK-NEXT:    inputs: [  ],
// CHECK-NEXT:    outputs: [ 0 ],

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.VarHandleOp"() {container = "c", shared_name = "n"} : () -> tensor<!tf_type.resource<tensor<8xf32>>>
    %1:2 = tf_executor.island wraps "tf.StatefulPartitionedCall"(%0#0) {Tin = ["tfdtype$DT_RESOURCE"], Tout = ["tfdtype$DT_RESOURCE"], config = "", config_proto = "", executor_type = "", f = @foo} : (tensor<!tf_type.resource<tensor<8xf32>>>) -> tensor<!tf_type.resource<tensor<8xf32>>> loc("call_foo")

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x3xf32>

// CHECK-NEXT:      buffer: 6,
// CHECK-NEXT:      name: "StatefulPartitionedCall:0",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:      },
// CHECK-NEXT:      shape_signature: [ -1, 5 ],
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    }, {
// CHECK-NEXT:      shape: [ 1, 5 ],
// CHECK-NEXT:      buffer: 7,
// CHECK-NEXT:      name: "StatefulPartitionedCall:1",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:

  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "spc1"
  op: "StatefulPartitionedCall"
  input: "input0"
  input: "args_0"
  attr {
    key: "Tin"
    value {
      list {
        type: DT_FLOAT
        type: DT_RESOURCE
      }
    }
  }
  attr {
    key: "Tout"

func.func @convert_xla_must_compile(%arg0: tensor<i32>) -> tensor<i32> {
  // CHECK: "tf.StatefulPartitionedCall"(%arg0) <{config = "", config_proto = "", executor_type = "", f = @stateful_pcall_func}> {_xla_compile_device_type = "CPU", device = "/device:CPU:0"} : (tensor<i32>) -> tensor<i32>

// CHECK: [[r:%.*]] = "tf.StatefulPartitionedCall"([[x]], [[const]])
// CHECK-SAME: f = @then}
// CHECK: return [[r]]

// CHECK-LABEL: func private @else_removed_const_args_0
// CHECK-SAME: ([[x:%.*]]: tensor<i32>)
// CHECK: [[const:%.*]] = "tf.Const"
// CHECK-SAME: value = dense<10> : tensor<i32>
// CHECK: [[r:%.*]] = "tf.StatefulPartitionedCall"([[x]], [[const]])
// CHECK-SAME: f = @else}

  "tf.PartitionedCall"() {config = "",  config_proto = "", executor_type = "", f = @g} : () -> ()
  // CHECK: "tf.StatefulPartitionedCall"() <{config = "",  config_proto = "", executor_type = "", f = @[[NEWG:.+]]}> : () -> ()
  "tf.StatefulPartitionedCall"() {config = "",  config_proto = "", executor_type = "", f = @g} : () -> ()
  func.return
}

// CHECK: func.func @g()

    (tensor<1x10xf32> {tf_saved_model.index_path = ["output_0"]}, tensor<1x10xi64> {tf_saved_model.index_path = ["output_1"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_x:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %handle_0 = "tf.VarHandleOp"() {container="c", shared_name="a"} : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>

  # CHECK-SAME:   tensor<f32> {tf_saved_model.index_path = [1]})
  # CHECK-SAME: attributes{{.*}}tf_saved_model.exported_names = ["callee"]
  # CHECK:        "tf.StatefulPartitionedCall"{{.*}}f = @[[CALLEE_INTERNAL:[a-zA-Z_0-9]+]]
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<f32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]},