func.func @infer_device_launch(%arg0: tensor<1x8x2xi32>) -> (tensor<*xf32>, tensor<*xf32>) {
    %0 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %1 = "tf_device.launch"() ({
      %2 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x8x2xi32>) -> tensor<1x8x2xf32>
      tf_device.return %2 : tensor<1x8x2xf32>
    // CHECK: () -> tensor<1x8x2xf32>
    }) {device = "/device:CPU:0"} : () -> tensor<*xf32>

  %split_dim_2 = arith.constant dense<1> : tensor<1xi32>
  %4, %5 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x2xf32>)
  %6:2 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x?xf32>)

func.func @expandDims(%arg0: tensor<2x2xf32>, %arg1: tensor<i32>) -> tensor<1x2x2xf32> {
  %0 = "tf.ExpandDims"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<i32>) -> tensor<1x2x2xf32>
  func.return %0 : tensor<1x2x2xf32>

// CHECK-LABEL:expandDims
// CHECK:  "tfl.expand_dims"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<i32>) -> tensor<1x2x2xf32>
}

func.func @squeezeDefault(%arg0: tensor<1x2x2xf32>) -> tensor<2x2xf32> {

    // CHECK:      metadata
    // CHECK-SAME: args
    // CHECK-SAME: shape
    // CHECK-SAME: dim
    // CHECK-SAME: size: 1
    // CHECK-SAME: dim
    // CHECK-SAME: size: 2
    // CHECK-SAME: dim
    // CHECK-SAME: size: 3

    func.return %0: tensor<1x2x3xi32>
  }

    return success();
  }
};

// Fuses Unpack with proceeding Concatenation to Reshape if output type has
// static shape and activation function is none. For example:
//
//   // %input: tensor<1x3x2xf32>
//   %unpack:3 = "tfl.unpack"(%input) {axis = 1 : i32, num = 3 : i32}
//   %res = "tfl.concatenation"(%unpack#0, %unpack#1, %unpack#2)
//        {axis = -1 : i32, fused_activation_function = "NONE"}
//

func.func @invalidSelect(%arg0: tensor<1x8xi1>, %arg1: tensor<1x8x8xi32>, %arg2: tensor<1x8x8xi32>) -> tensor<1x8x8xi32> {
  // expected-error @+1 {{requires that pred is a scalar OR has the same rank as t and e OR is a vector}}
  %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<1x8xi1>, tensor<1x8x8xi32>, tensor<1x8x8xi32>) -> tensor<1x8x8xi32>
  func.return %0: tensor<1x8x8xi32>
}

// -----