func check(bounds image.Rectangle, pix0, pix1 []byte, stride0, stride1 int) error {
	if stride0 <= 0 || stride0%8 != 0 {
		return fmt.Errorf("bad stride %d", stride0)
	}
	if stride1 <= 0 || stride1%8 != 0 {
		return fmt.Errorf("bad stride %d", stride1)
	}
	// Compare the two pix data, one 8x8 block at a time.
	for y := 0; y < len(pix0)/stride0 && y < len(pix1)/stride1; y += 8 {
		for x := 0; x < stride0 && x < stride1; x += 8 {

  Value stride_value = CreateScalarConstValue<int32_t>(builder, loc, stride);
  Value dilation_rate_value =
      CreateScalarConstValue<int32_t>(builder, loc, dilation_rate);

  Value effective_filter_size_op = scalar_add(
      scalar_mul(dilation_rate_value, scalar_sub(filter_size, one)), one);

  // output_size = (input_size + stride - 1) / stride
  Value output_size = scalar_div(

                            /*padding*/(GetAvgPoolOpPadAttr $old_val),
                            /*stride_h*/(GetI32At<0> (GetAsVectorAttr<"stride"> $attrs)),
                            /*stride_w*/(GetI32At<1> (GetAsVectorAttr<"stride"> $attrs)),
                            /*fused_activation_function*/TFL_AF_None,
                            (returnType (GetNhwcReturnTypeFromNchw $old_val))), 

  // CHECK: %[[VAL3:.+]] = "tfl.conv_3d"(%arg0, %[[VAL2]], %[[VAL0]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}>
  // CHECK: return %[[VAL3]]

              IsIntList1XY1:$ksize,
              IsIntList1XY1:$strides,
              $padding,
              $explicit_paddings,
              IsDataFormatNHWC:$format),
          (TFL_MaxPool2DOp $value,
              /*padding=*/$padding,
              /*stride_w=*/ExtractI32At<2>:$strides,
              /*stride_h=*/ExtractI32At<1>:$strides,
              /*filter_width=*/ExtractI32At<2>:$ksize,

    PatternRewriter& rewriter, llvm::StringMap<Attribute>& identifier_to_attr) {
  ArrayAttr stride = mlir::dyn_cast<ArrayAttr>(identifier_to_attr["strides"]);
  const int stride_h = mlir::cast<IntegerAttr>(stride[1]).getInt();
  const int stride_w = mlir::cast<IntegerAttr>(stride[2]).getInt();
  return rewriter.getI64ArrayAttr({stride_h, stride_w});
}

Attribute GetLhsDilationValue(PatternRewriter& rewriter,

  %dconv = "tfl.depthwise_conv_2d"(%arg0, %w, %b) {depth_multiplier = 4 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>)...

   // Unsupported strides
   %3 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [2, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x30x30x12xf32>

// CHECK:           %[[VAL_5:.*]] = "tfl.average_pool_2d"(%[[VAL_4]]) <{filter_height = 3 : i32, filter_width = 3 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x3xf32>) -> tensor<1x4x4x3xf32>
// CHECK:           %[[VAL_6:.*]] = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>

  %dconv = "tfl.depthwise_conv_2d"(%0, %w, %b) {depth_multiplier = 4 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>)...