%1 = "tfl.neg"(%arg0) : (tensor<2x3xf32>) -> tensor<2x3xf32>
  %2 = "tfl.relu"(%1) : (tensor<2x3xf32>) -> tensor<2x3xf32>
  %3 = "tfl.mul"(%alpha, %2) {fused_activation_function = "NONE"} : (tensor<f32>, tensor<2x3xf32>) -> tensor<2x3xf32>
  %4 = "tfl.add"(%0, %3) {fused_activation_function = "NONE"} : (tensor<2x3xf32>, tensor<2x3xf32>) -> tensor<2x3xf32>
  func.return %4 : tensor<2x3xf32>

// CHECK:       return %[[RES]]

func.func @torch_index_select(%arg0: tensor<2x1xf32>, %arg1: tensor<2xi32>) -> tensor<2x1xf32> {
  %0 = "mhlo.torch_index_select"(%arg0, %arg1) {
    batch_dims = 0 : i64, dim = 0 : i64
  } : (tensor<2x1xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  func.return %0 : tensor<2x1xf32>
}

// CHECK-LABEL:   func @lowered_cumsum(

// -----

// CHECK-LABEL: func @erf
func.func @erf(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
  // CHECK: mhlo.erf %arg0 : tensor<2x3xf32>
  %0 = "tf.Erf"(%arg0) : (tensor<2x3xf32>) -> tensor<2x3xf32>
  func.return %0 : tensor<2x3xf32>
}

//===----------------------------------------------------------------------===//
// Erfc