// CHECK:           return %[[VAL_2]] : tensor<1x2xi32>
// CHECK:         }
func.func @broadcast_add_chlo(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi32> {
  %0 = "chlo.broadcast_add"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi32>
  func.return %0 : tensor<1x2xi32>
}

func.func @pack(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2x2xi32> {
  // CHECK: mhlo.reshape {{.*}} : (tensor<2xi32>) -> tensor<1x2xi32>
  // CHECK: mhlo.reshape {{.*}} : (tensor<2xi32>) -> tensor<1x2xi32>
  // CHECK: "mhlo.concatenate"({{.*}}) <{dimension = 0 : i64}> : (tensor<1x2xi32>, tensor<1x2xi32>) -> tensor<2x2xi32>

  %0 = "tf.Pack"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2x2xi32>

// CHECK-LABEL: fuseTileWithBinaryOp
func.func @fuseTileWithBinaryOp(%arg0: tensor<1x1xf32>) -> tensor<1x2xf32> {
  %cst = arith.constant dense<[[1,2]]> : tensor<1x2xi32>
  %cst1 = arith.constant dense<[[3.0, 4.0]]> : tensor<1x2xf32>
  %0 = "tfl.sqrt"(%arg0) : (tensor<1x1xf32>) -> tensor<1x1xf32>
  %1 = "tfl.tile"(%0, %cst) : (tensor<1x1xf32>, tensor<1x2xi32>) -> tensor<1x2xf32>

  %261 = "tf.Shape"(%254) {device = ""} : (tensor<?x?xi64>) -> tensor<2xi32>
  %262 = "tf.StridedSlice"(%261, %6, %15, %16) {begin_mask = 0 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 1 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<0xi32>