// 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]])

 = "args_7"}, %arg8: tensor<?x?xf32> {tf._user_specified_name = "args_8"}, %arg9: tensor<?x300xi32> {tf._user_specified_name = "args_9"}, %arg10: tensor<?x300xi32> {tf._user_specified_name = "args_10"}, %arg11: tensor<?x300xf32> {tf._user_specified_name = "args_11"}, %arg12: tensor<?x300xf32> {tf._user_specified_name = "args_12"}, %arg13: tensor<?x!tf_type.string> {tf._user_specified_name = "args_13"}, %arg14: tensor<?x!tf_type.string> {tf._user_specified_name = "args_14"}, %arg15: tensor<!tf_ty...

# 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"

// Start step 0
C_0 = cond(args_0)
N_0 = non_tpu(args_0)
if (C_0) {
   F_0 = forward(args_0, N_0)
   T_0 = core_tpu(args_0, N_0, F_0)
   // B_0 = backward() is not evaluated here.
}

args_1 = update_args(args_0, N_0, T_0)

// Start step 1
C_1 = cond(args_1)
N_1 = non_tpu(args_1)
if (C_1) {
   F_1 = forward(args_1, N_1)
   // T_1 = core_tpu() is not evaluated here.

// CHECK:             %{{.*}}, %[[STACK_PUSH_CONTROL:.*]] = tf_executor.island wraps "tf.StackPushV2"(%[[STACK_HANDLE]], %[[ARG_1]]) : (tensor<!tf_type.resource<tensor<f32>>>, tensor<f32>) -> tensor<f32>
// CHECK:             %[[ADD:.*]], %{{.*}} = tf_executor.island wraps "tf.Add"(%[[ARG_1]], %[[ARG_1]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>

// CHECK-LABEL: func @non_interfering_accesses
// CHECK-SAME: %[[ARG_0:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<32xf32>>>
// CHECK-SAME: %[[ARG_1:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<64xf32>>>
// CHECK-SAME: %[[ARG_2:[a-z0-9]+]]: tensor<32xf32>
// CHECK-SAME: %[[ARG_3:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<8xf32>>>
// CHECK-SAME: %[[ARG_4:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<2xf32>>>

// CHECK-SAME: %[[ARG_1]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_3]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_5]] as %{{[a-z0-9]*}}


// Test that the `is_mirrored_variable` attribute is preserved in the
// tf_device.replicate op.
// CHECK-LABEL: func @mirrored_variables

    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:

        final ParameterizedClassDesc[] args2 = arg2.getArguments();
        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 {