%1 = "tf.Transpose"(%arg0, %0) : (tensor<1x3x32x32xf32>, tensor<4xi32>) -> tensor<1x32x32x3xf32>

  // Compute in NHWC
  %2 = "tf.Conv2D"(%1, %arg1)
    {
      data_format = "NHWC",
      padding = "SAME",
      strides = [1, 1, 1, 1],
      dilations = [1, 1, 1, 1]
    } : (tensor<1x32x32x3xf32>, tensor<1x1x3x8xf32>) -> tensor<1x32x32x8xf32>

  // Convert result back: NHWC -> NCHW

         strides = [5, 6, 7, 8]
       } : (tensor<1x32x32x3xf32>, tensor<1x1x3x8xf32>) -> tensor<?x?x?x?xf32>

  func.return %0 : tensor<?x?x?x?xf32>
}

// CHECK-LABEL: func @transposeConv2DBackpropFilter
func.func @transposeConv2DBackpropFilter(
  %input: tensor<1x32x32x3xf32>,
  %filter_sizes: tensor<4xi32>,
  %out_backprop: tensor<1x32x32x8xf32>
) -> tensor<1x1x3x8xf32> {

      // CHECK-OPT: %{{.*}} = stablehlo.custom_call @tf.ResizeBilinear(%arg0, %cst) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<1x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>
      %1 = "tf.ResizeBilinear"(%arg0, %0) {

  func.return %0 : tensor<?x32x32x16xf32>
}

func.func @testDepthwiseConv2D(tensor<1x112x112x3xf32>, tensor<1x3x3x32xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32> {
^bb0(%arg0: tensor<1x112x112x3xf32>, %arg1: tensor<1x3x3x32xf32>, %arg2: tensor<32xf32>):

  // CHECK-SAME: dilations = [1, 3, 4, 2]
  // CHECK-SAME: explicit_paddings = [1, 2, 5, 6, 7, 8, 3, 4]
  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 7, 8, 6]
  // CHECK-SAME: (tensor<1x32x32x3xf32>, tensor<1x1x3x8xf32>) -> tensor<1x7x6x8xf32>

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%[[CONV2D]], %[[RES_PERM]])