// CHECK:  return
}

func.func @addV2(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  func.return %0 : tensor<1xi32>

// CHECK-LABEL: addV2
// CHECK:  tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xi32>
}

func.func @addV2I16(%arg0: tensor<1xi16>, %arg1: tensor<1xi16>) -> tensor<1xi16> {

// CHECK:           [[VAL_23:%.*]] = "tf.Reshape"([[VAL_15]]#1, [[VAL_22]]) : (tensor<1x10xf32>, tensor<1xi32>) -> tensor<10xf32>
// CHECK-DAG:       [[VAL_24:%.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK:           [[VAL_25:%.*]] = "tf.Reshape"([[VAL_15]]#2, [[VAL_24]]) : (tensor<1x10xf32>, tensor<1xi32>) -> tensor<10xf32>

  %2 = "tf.AddN"(%arg0, %1, %1) : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  %3 = "tf.AddN"(%1, %arg0, %1) : (tensor<2xi32>, tensor<2xi32> , tensor<2xi32>) -> tensor<2xi32>
  %4 = "tf.AddN"(%1, %1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  %5 = "tf.AddN"(%arg0, %1, %0) : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %2, %3, %4, %5: tensor<2xi32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>
}

// -----

func.func @testFusedActivationFunction(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) {
  // CHECK: "NONE"
  %0 = tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<4xi32>
  // CHECK: "RELU"
  %1 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU"} : tensor<4xi32>
  // CHECK: "RELU_N1_TO_1"

    ```mlir
      %2 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>
      %3 = "tf.B"(%2) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32>
      %4 = "tf.C"(%2, %3) {device = "tpu0"} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
      %5 = "tf.D"(%4) : (tensor<?xi32>) -> tensor<?xi32>
    ```

    After the pass, we will have:

    ```mlir
      %0 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

// CHECK-DAG: %[[BROADCAST_DIM:.+]] = arith.constant dense<{{\[3, 2, 1, 1\]}}> : tensor<4xi32>
// CHECK-DAG: %[[EXPAND_DIM1:.+]] = arith.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[EXPAND_DIM0:.+]] = arith.constant dense<2> : tensor<1xi32>
// CHECK: %[[EXPAND0:.+]] = "tfl.expand_dims"(%[[ARG0]], %[[EXPAND_DIM0]]) : (tensor<1x2x!quant.uniform<i8:f32, 2.000000e+00:3>>, tensor<1xi32>) -> tensor<1x2x1x!quant.uniform<i8:f32, 2.000000e+00:3>>

  // The first Operand is assumed to be a TensorType around a variant with a
  // single subtype (e.g. tensor<!tf_type.variant<tensor<2xi32>>>). We will
  // copy this type to the first result, and copy the singular variant subtype
  // to the second result (tensor<2xi32>).
  DCOMMENT_OP(op, "Inferring shape for TensorListPopBackOp.");

  auto src_list_handle_t =