# CHECK-SAME: allow_soft_placement
# CHECK-SAME: control_outputs = ""
# CHECK-SAME: inputs = "args_0,args_1,args_2,args_3"
# CHECK-SAME: outputs = "rets_0,rets_1"
# CHECK:          %[[ISLAND_0:.*]], %[[ISLAND_0_control:.*]] = tf_executor.island wraps "tf.Const"
# CHECK:          %[[ISLAND_1:.*]], %[[ISLAND_1_control:.*]] = tf_executor.island wraps "tf.Identity"(%[[ISLAND_0]])

    // CHECK-NEXT: "tf.MakeIterator"(%[[ARG_0]], %[[ANON_ITER]])
    // CHECK-NEXT: %[[COND:.*]] = "tf.Const"
    // CHECK-NEXT: %[[WHILE:[0-9]*]]:5 = "tf.WhileRegion"(%[[COND]], %[[ARG_1]], %[[ARG_2]], %[[ARG_3]], %[[ARG_4]])
    // CHECK-NEXT:   ^bb0(%[[ARG_5:.*]]: tensor<i1>, %[[ARG_6:.*]]: tensor<i64>, %[[ARG_7:.*]]: tensor<i32>, %[[ARG_8:.*]]: tensor<!tf_type.resource<tensor<64xf32>>>, %[[ARG_9:.*]]: tensor<!tf_type.resource<tensor<128xf32>>>)

        then:
        output1.toString().contains(ARG_1)
        output1.toString().contains(ARG_2)

        when:
        String output2 = runBuild { launcher -> launcher.addArguments(["-P$ARG_1" as String]).addArguments(["-P$ARG_2" as String]) }

        then:
        output2.toString().contains(ARG_1)
        output2.toString().contains(ARG_2)
    }

    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "args_0"
  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 {

  }

  // CHECK-LABEL: func private @main_0
  // CHECK-SAME: %[[ARG_0:.*]]: tensor<10xbf16>, %[[ARG_1:.*]]: tensor<10xbf16>, %[[ARG_2:.*]]: tensor<6xi32>
  func.func private @main_0(
      %arg0: tensor<10xf32>, %arg1: tensor<10xf32>, %arg2: tensor<6xi32>
    ) -> (tensor<10xf32>, tensor<6xi32>) attributes {_from_xla_call_module} {
    // CHECK: %[[ADD:.*]] = stablehlo.add %[[ARG_0]], %[[ARG_1]] : tensor<10xbf16>

        assertThat(args2.length, is(1));

        final ParameterizedClassDesc arg2_1 = args2[0];
        assertThat(arg2_1.getRawClass(), is(sameClass(Integer.class)));
    }

    /**
     * @throws Exception
     */
    @Test
    public void testMethodParameterType() throws Exception {
        final Map<TypeVariable<?>, Type> map = ParameterizedClassDescFactory.getTypeVariables(Hoge.class);

    func.return %4 : tensor<!tf_type.variant>
  }
  // CHECK-LABEL: __inference_Dataset_flat_map_lambda_190
  func.func private @__inference_Dataset_flat_map_lambda_190(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "__inference_Dataset_flat_map_lambda_19", tf._tf_data_function = true, tf.signature.is_stateful} {

    func.return %4, %6 : tensor<!tf_type.variant>, tensor<!tf_type.variant>
  }
  // CHECK-LABEL: @funcB_renamed
  func.func private @funcB_renamed(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "funcB", tf._tf_data_function = true, tf.signature.is_stateful} {
    %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i64>} : () -> tensor<i64>

# CHECK: "tf.AssignVariableOp"(%[[ARG_0]], %[[RAND_STD_NORMAL]]){{.*}}: (tensor<!tf_type.resource<tensor<1x3xf32>>>, tensor<1x3xf32>) -> ()
# CHECK: return

# The function for the signature "key".
# CHECK: func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME: %[[ARG_1:.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]}
# CHECK-SAME: %[[ARG_2:.*]]: tensor<!tf_type.resource<tensor<1x3xf32>>> {tf_saved_model.bound_input = @y}

// CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<*xf32>, %[[ARG_1:[a-z0-9]*]]: tensor<*xi32>)
func.func @return_multiple_operands(%arg_0: tensor<*xf32>, %arg_1: tensor<*xi32>) {
  %result:2 = "tf_device.launch"() ({
// CHECK:   tf_device.return %[[ARG_0]], %[[ARG_1]] : tensor<*xf32>, tensor<*xi32>
    tf_device.return %arg_0, %arg_1 : tensor<*xf32>, tensor<*xi32>
  }) {device = "device"} : () -> (tensor<*xf32>, tensor<?xi32>)
  func.return