}

func.func @LRN(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> {
  %0 = "tf.LRN"(%arg0) {depth_radius = 5 :i64, bias = 1.0 :f32, alpha = 1.0 : f32, beta = 0.5 :f32} : (tensor<2x3x4x5xf32>) -> (tensor<2x3x4x5xf32>)
  func.return %0: tensor<2x3x4x5xf32>

  // CHECK-LABEL: LRN

func.func private @dense_image_warp_invalid_input_type(%arg0: tensor<2x4x4x1xi32>, %arg1: tensor<2x4x4x2xf32>) -> tensor<2x4x4x1xf32> attributes {tf._implements = "addons:DenseImageWarp"}

// expected-warning @+1 {{Flow should be a 4D float tensor}}
func.func private @dense_image_warp_invalid_flow_type(%arg0: tensor<2x4x4x1xf32>, %arg1: tensor<2x4x4x2xi32>) -> tensor<2x4x4x1xf32> attributes {tf._implements = "addons:DenseImageWarp"}

// -----

func.func @transpose_output_type_bad(%arg0 : tensor<3x4x5x6xi32>) -> tensor<3x4x5x6xi32> {
  %cst = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>
  // expected-error @+1 {{expect output type tensor<3x6x4x5xi32>, got tensor<3x4x5x6xi32>}}
  %0 = "tfl.transpose"(%arg0, %cst) : (tensor<3x4x5x6xi32>, tensor<4xi32>) -> tensor<3x4x5x6xi32>
  func.return %0 : tensor<3x4x5x6xi32>
}


// -----