xla::ConvolutionDimensionNumbers ConvertConvolutionDimensionNumbers(
    const ::tensorflow::UniformQuantizedConvolutionDimensionNumbersAttr
        &dnums_input) {
  xla::ConvolutionDimensionNumbers dnums;
  dnums.set_input_batch_dimension(dnums_input.input_batch_dimension());
  dnums.set_input_feature_dimension(dnums_input.input_feature_dimension());
  for (auto value : dnums_input.input_spatial_dimensions()) {

                  num_dims));
    return {};
  }

  xla::ConvolutionDimensionNumbers dnums;
  // Input: [N, H, W, C] for Conv2D or [N, D, H, W, C] for Conv3D.
  dnums.set_input_batch_dimension(0);
  dnums.set_input_feature_dimension(num_dims - 1);
  // Kernel: [K, K, I, O] for Conv2D or [K, K, K, I, O] for Conv3D.
  dnums.set_kernel_input_feature_dimension(num_dims - 2);

  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
    VT_WINDOW_STRIDES = 4,
    VT_PADDING = 6,
    VT_LHS_DILATION = 8,
    VT_RHS_DILATION = 10,
    VT_WINDOW_REVERSAL = 12,
    VT_INPUT_BATCH_DIMENSION = 14,
    VT_INPUT_FEATURE_DIMENSION = 16,
    VT_INPUT_SPATIAL_DIMENSIONS = 18,
    VT_KERNEL_INPUT_FEATURE_DIMENSION = 20,
    VT_KERNEL_OUTPUT_FEATURE_DIMENSION = 22,