%0 = "tf.Const"() {value = dense<0.111111112> : tensor<3x3x1x1xf32>} : () -> tensor<3x3x1x1xf32>
  %1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<1x520x520x1xf32>} : () -> tensor<1x520x520x1xf32>
  %2 = "tf.DepthwiseConv2dNative"(%1, %0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<1x520x520x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x520x520x1xf32>

func.func @testSpaceToDepthF32(%arg0: tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> {
  // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32>
  // CHECK: "tfl.space_to_depth"(%[[ARG]]) <{block_size = 2 : i32}> : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  %0 = "tfl.space_to_depth"(%arg0) {block_size = 2: i32} : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  func.return %0 : tensor<1x1x1x4xf32>
}

// -----

}

func.func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> {
  %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64,  data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32>
  func.return %0 : tensor<1x2x2x1xf32>

  // CHECK-LABEL: depth_to_space
  // CHECK: %[[ARG:.*]]: tensor<1x1x1x4xf32>

  %cst_2 = arith.constant dense<1.1> : tensor<3x2x1x1xf32>
  %0 = "tfl.add"(%arg0, %cst) {fused_activation_function = "RELU6"} : (tensor<1x3x1x1xf32>, tensor<1xf32>) -> tensor<1x3x1x1xf32>
  %1 = "tfl.conv_2d"(%0, %cst_2, %cst_1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x3x1x1xf32>, tensor<3x2x1x1xf32>, tensor<3xf32>) -> tensor<1x2x1x3xf32>

^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x4xf32>) :
  %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {device = "", name = "MobilenetV2/expanded_conv/depthwise/depthwise", T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x30x30x12xf32>

// CHECK-LABEL: conv_valid_padding
func.func @conv_valid_padding(%arg0: tensor<1x4x5x1xf32>, %arg1: tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32> {
  // CHECK: mhlo.convolution(%arg0, %arg1)

  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<1x4x5x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32>
  func.return %0 : tensor<1x2x3x1xf32>
}

// -----