// CHECK:             mhlo.return %[[VAL_5]] : tensor<f32>
// CHECK:           }) : (tensor<20x6xf32>, tensor<4xi32>, tensor<4x6xf32>) -> tensor<20x6xf32>
// CHECK:           return %[[VAL_3]] : tensor<20x6xf32>
// CHECK:         }
func.func @convert_scatter_update(%arg0: tensor<20x6xf32>, %arg1: tensor<4xi32>, %arg2: tensor<4x6xf32>) -> tensor<20x6xf32> {
  %0 = "mhlo.scatter"(%arg0, %arg1, %arg2) ({
  ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>):

  // return %[[RES1]], %[[RES2]], %[[RES3]]
  func.return %0#0, %0#1, %0#2 : tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>
}

// -----

// CHECK-LABEL: func @unpack_dynamic
func.func @unpack_dynamic(%arg0: tensor<?x?x2xf32>) -> (tensor<?x?xf32>, tensor<?x?xf32>) {

  %cst = arith.constant dense<3.0> : tensor<40xf32>
  %cst2 = arith.constant dense<2.0> : tensor<40xf32>

  %0 = "tfl.fully_connected" (%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> (tensor<40x40xf32>)
  %1 = "tfl.add"(%0, %cst2) {fused_activation_function = "NONE"} : (tensor<40x40xf32>, tensor<40xf32>) -> tensor<40x40xf32>

//                (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>