%0 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @partitioned_called_func} : (tensor<20xi32>) -> tensor<*xi32>
    // CHECK: tf.StatefulPartitionedCall
    // CHECK-SAME: (tensor<20xi32>) -> tensor<20xi32>
    %1 = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @stateful_partitioned_call_func} : (tensor<20xi32>) -> tensor<*xi32>
    // CHECK: tf.TPUPartitionedCall

func.func @func_with_device_training_loop(%arg0: tensor<*xi32>, %arg1: tensor<*xi1>) -> (tensor<*xi32>, tensor<*xi1>) {
  %1:2 = "tf.StatefulPartitionedCall"(%arg0){f= @func_body, config="", config_proto="", executor_type=""}
         : (tensor<*xi32>) -> (tensor<*xi32>, tensor<*xi1>)

  func.return %arg0 : tensor<i32>
}

// -----

// Test valid tf.StatefulPartitionedCall
// CHECK-LABEL: func @testValidStatefulPartitionedCall
func.func @testValidStatefulPartitionedCall(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @pcall_func} : (tensor<i32>) -> (tensor<i32>)

  // CHECK: call @callee() : () -> ()
  // CHECK: call @callee() : () -> ()
  "tf.PartitionedCall"() {config = "", config_proto = "", executor_type = "", f = @callee} : () -> ()
  "tf.StatefulPartitionedCall"() {config = "", config_proto = "", executor_type = "", f = @callee} : () -> ()
  "tf.LegacyCall"() {config = "", config_proto = "", executor_type = "", f = @callee} : () -> ()
  func.return
}

      "SpaceToBatchND",
      "SpaceToDepth",
      "SparseMatMul",
      "SparseToDense",
      "StackCloseV2",
      "StackPopV2",
      "StackPushV2",
      "StackV2",
      "StatefulPartitionedCall",
      "StatefulStandardNormalV2",
      "StatefulTruncatedNormal",
      "StatefulUniform",
      "StatefulUniformFullInt",
      "StatefulUniformInt",
      "StatelessCase",

    TF_Tensor:$output
  );

  TF_DerivedResultTypeAttr dtype = TF_DerivedResultTypeAttr<0>;
  DerivedAttr shape = TF_DerivedResultShapeAttr;
}

def TF_StatefulPartitionedCallOp : TF_Op<"StatefulPartitionedCall",
      [CallOpInterface, DeclareOpInterfaceMethods<SymbolUserOpInterface>]> {
  let summary =
    "returns `f(inputs)`, where `f`'s body is placed and partitioned.";

  let description = [{

        }
      }
      attr {
        key: "shape"
        value {
          shape {
          }
        }
      }
    }
    node {
      name: "StatefulPartitionedCall"
      op: "StatefulPartitionedCall"
      input: "saver_filename"
      input: "Const_1"
      attr {
        key: "Tin"
        value {
          list {
            type: DT_STRING
            type: DT_STRING

  return false;
}

// Returns whether the current op is not supported by the TF Lite runtime.
static bool IsUnsupportedFlexOp(const std::string& op_name) {
  return op_name == "PartitionedCall" || op_name == "StatefulPartitionedCall";
}

// Create description of operation that could not be converted.
static std::string GetOpDescriptionForDebug(Operation* inst) {
  const int kLargeElementsAttr = 16;
  std::string op_str;

  %2 = "tf.If"(%0, %arg0, %arg1) {then_branch = @add, else_branch = @sub, output_shapes = [#tf_type.shape<>], device = "noodle", is_stateless = true} : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
  // CHECK: %1 = "tf.StatefulPartitionedCall"(%0, %arg1)
  // CHECK-SAME: f = @add
  // CHECK-SAME: _underscore_attr = "something"
  // CHECK-SAME: device = "noodle"