func.return %2, %3, %4, %5: tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>
}

// CHECK-LABEL: func @addNWithZerosInt
func.func @addNWithZerosInt(%arg0: tensor<2xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) {
  %0 = "tf.Const"() {value = dense<1> : tensor<2xi32>} : () -> tensor<2xi32>
  %1 = "tf.Const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>

     %t2 = "tf.Acosh"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
    "tf.Yield"(%t0, %t1, %t2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()
    }, {
     %e0 = "tf.Neg"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e1 = "tf.Relu"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e2 = "tf.Sin"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     "tf.Yield"(%e0, %e1, %e2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()

  func.return %0#0 : tensor<2xi32>

// CHECK-LABEL: unpack3Tensors
// CHECK: "tfl.unpack"(%arg0) <{axis = 1 : i32, num = 3 : i32}> : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>)
}

func.func @unpackNegAxis(%arg0: tensor<2x3xi32>) -> tensor<2xi32> {
  %0:3 = "tf.Unpack"(%arg0) {axis = -1 : i64} : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>)

  func.return %0#0 : tensor<2xi32>
}

// -----

func.func @unpack(%arg0: tensor<*xi32>) -> (tensor<*xi32>, tensor<*xi32>) {
  %0:2 = "tfl.unpack"(%arg0) {axis = 1 : i32, num = 2 : i32} : (tensor<*xi32>) -> (tensor<*xi32>, tensor<*xi32>)
  func.return %0#0, %0#1 : tensor<*xi32>, tensor<*xi32>
}

// -----

  // CHECK-LABEL: func @main(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32>
  func.func @main(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<*xi32> {
    // CHECK: %[[RESULT:.*]] = "tf.AddV2"
    // CHECK-SAME: (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
    // CHECK: return %[[RESULT]] : tensor<1xi32>
    %0 = "tf.Cast"(%arg0) : (tensor<1xi32>) -> tensor<*xi32>
    %1 = "tf.Cast"(%arg1) : (tensor<1xi32>) -> tensor<*xi32>

// 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>

        %5:2 = "tf.C"(%4) : (tensor<2xi32>) -> (tensor<2xi32>, tensor<2xi32>)
        tf_device.return %5#0, %5#1 : tensor<2xi32>, tensor<2xi32>
      }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> (tensor<2xi32>, tensor<2xi32>)
      tf_device.return %2#1 : tensor<2xi32>
    }
    func.return %1 : tensor<2xi32>
  }

// CHECK-DAG: %[[START:.+]] = arith.constant dense<{{\[1, 2\]}}> : tensor<2xi32>
// CHECK-DAG: %[[SIZE:.+]] = arith.constant dense<2> : tensor<2xi32>
// CHECK: %[[SLICE:.+]] = "tfl.slice"(%[[ARG0]], %[[START]], %[[SIZE]]) : (tensor<3x4x!quant.uniform<i8:f32, 2.000000e+00:-1>>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x2x!quant.uniform<i8:f32, 2.000000e+00:-1>>
// CHECK: return %[[SLICE]]

// -----

    ```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>

  // 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 =