func.func @concat_v2(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
  %2 = "mhlo.concatenate"(%arg0, %arg1) <{dimension = 0 : i64}> : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<6x3xf32>
  func.return %2 : tensor<6x3xf32>
}

// CHECK-LABEL:   func @concat_v2_1d_axis(
// CHECK-SAME:                            %[[VAL_0:.*]]: tensor<3x3xf32>,
// CHECK-SAME:                            %[[VAL_1:.*]]: tensor<3x3xf32>) -> tensor<3x6xf32> {

  %1 = "tfl.add"(%0, %cst2) {fused_activation_function = "NONE"} : (tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4xf32>

  func.return %1 : tensor<4x4xf32>

  // CHECK-DAG: %[[unit:.*]] = "tfl.no_value"() <{value}> : () -> none
  // CHECK-DAG: %[[filter:.*]] = arith.constant dense<{{.*}}> : tensor<4x4xf32>

  %0:3 = "tf.SplitV"(%input, %split_sizes, %split_dim) : (tensor<4x6xf32>, tensor<3xi32>, tensor<i32>) -> (tensor<4x1xf32>, tensor<4x2xf32>, tensor<4x3xf32>)
  // CHECK: return %[[ONE]], %[[TWO]], %[[THREE]]
  func.return %0#0, %0#1, %0#2 : tensor<4x1xf32>, tensor<4x2xf32>, tensor<4x3xf32>
}

// -----

//                (tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>)
//
// will be converted into:
//
//   %0 = "mhlo.slice"(%input) {
//             limit_indices = dense<[4, 2]> : tensor<2xi64>,
//             start_indices = dense<0> : tensor<2xi64>,
//             strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>