// CHECK-SAME:       _xla_compile_device_type = "TPU"
    // CHECK:            "tf.Yield"(%[[FUNC_CALL]]#0, %[[FUNC_CALL]]#1)
    // CHECK:            "tf.Yield"(%[[ARG_11]], %[[ARG_12]])
    // CHECK:          "tf.Yield"(%[[HAS_VALUE]], %[[IF]]#0, %[[IF]]#1, %[[ARG_13]], %[[ARG_14]])

        type: DT_INT32
        type: DT_INT64
      }
    }
  }
}
node {
  name: "MultiDeviceIteratorGetNextFromShard"
  op: "MultiDeviceIteratorGetNextFromShard"
  input: "args_2"
  input: "args_3"
  input: "args_4"
  attr {
    key: "output_shapes"
    value {
      list {
        shape {
          dim {
            size: 5
          }
          dim {
            size: 40

# CHECK-SAME: _xla_compile_device_type = "GPU"
# 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"

func.func @main(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>>, %arg2: tensor<*xf32>, %arg3: tensor<2x4x6x8xi32>) -> (tensor<f32>, tensor<f32>)
attributes {tf.entry_function = {inputs = "args_0,args_1,args_2,args_3", outputs = "rets_0_RetVal,rets_1_RetVal"}} {
  %graph:2 = tf_executor.graph {

node {
  name: "args_0"
  op: "_Arg"
  device: "/CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "args_1"
  op: "_Arg"
  attr {
    key: "T"

  func.func @main(%arg0: tensor<*xf32> {tf.device = "/CPU:0"}, %arg1: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32> {tf.device = "/CPU:1"})
  attributes  {tf.entry_function = {inputs = "args_0,args_1", outputs = "rets_0,rets_1"}} {
    %0:2 = tf_executor.graph {

  func.func @main(%arg0: tensor<*xf32> {tf.aliasing_output = 0 : i64}, %arg1: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32> {tf.device = "/CPU:1"})
  attributes  {tf.entry_function = {inputs = "args_0,args_1", outputs = "rets_0,rets_1"}} {
    %0:2 = tf_executor.graph {

}

// CHECK: func @while_loop_test(%[[ARG_0:.*]]: tensor<i32>, %[[ARG_1:.*]]: tensor<i32>, %arg2: tensor<*xf32>)
func.func @while_loop_test(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<*xf32>) {
  // CHECK-NEXT: tf.WhileRegion
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  // CHECK-NEXT: bb0(%[[ARG_3:.*]]: tensor<i32>, %[[ARG_4:.*]]: tensor<*xf32>)
  ^bb0(%arg3: tensor<i32>, %arg4: tensor<*xf32>):

    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 {

        when:
        String output1 = runBuild { launcher -> launcher.addArguments("-P$ARG_1") }

        then:
        output1.contains(ARG_1)

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

        then:
        output2.contains(ARG_1)
    }

    def "Appends arguments multiple times"() {
        when: