func.func @split(%arg0: tensor<i32>, %arg1: tensor<1x4x3x3xf32>) -> tensor<1x4x3xf32> {
  %0:3 = "tf.Split"(%arg0, %arg1) : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
  func.return %0#0 : tensor<1x4x3xf32>

  // CHECK-LABEL: split
  // CHECK: "tfl.split"(%arg0, %arg1) <{num_splits = 3 : i32}> : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
}

func.func @testSelectV2WithWrongBroadcastableArguments(%cond : tensor<3x4xi1>, %arg0 : tensor<2x3x4xf32>, %arg1 : tensor<4x3xf32>) -> tensor<2x3x4xf32> {
  // expected-error @+1 {{'tfl.select_v2' op operands don't have broadcast-compatible shapes}}
  %0 = "tfl.select_v2"(%cond, %arg0, %arg1): (tensor<3x4xi1>, tensor<2x3x4xf32>, tensor<4x3xf32>) -> tensor<2x3x4xf32>
  func.return %0 : tensor<2x3x4xf32>
}

// -----

// CHECK-LABEL: topk

// %0:3 = "tf.SplitV"(%input, %split_sizes, %split_dim) :
//                   (tensor<4x6xf32>, tensor<3xi32>, tensor<i32>) ->
//                   (tensor<4x1xf32>, tensor<4x2xf32>, tensor<4x3xf32>)
//
// We will generate slices following slices:
// %0 = "mhlo.slice"(%input) {
//        limit_indices = dense<[4, 1]> : tensor<2xi64>,
//        start_indices = dense<0> : tensor<2xi64>,