= "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...

// 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

        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 {

// CHECK:       tf.WhileRegion
// CHECK:       ^bb0
// CHECK:       tf.OpA
// CHECK:       ^bb0([[ARG_2:%[a-zA-Z0-9_]+]]
// CHECK-SAME:  [[ARG_3:%[a-zA-Z0-9_]+]]: tensor<i32>)
// CHECK-NEXT:  [[RES_3:%.*]] = "tf.AddV2"([[ARG_2]], [[RES_1]])
// CHECK-NEXT:  [[RES_4:%.*]] = "tf.Div"([[ARG_3]], [[RES_2]])
// CHECK:       "tf.Yield"([[RES_3]], [[RES_4]])

// CHECK-LABEL: func @return_multiple_operands
// 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>)

# 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: -> (tensor<3x3xf32> {tf_saved_model.index_path = ["r"]})