input_batch_dimension = #vhlo.integer_v1<0 : i64>, 
                                             input_feature_dimension = #vhlo.integer_v1<3 : i64>,
                                             input_spatial_dimensions = #vhlo.tensor_v1<dense<[1, 2]> : tensor<2xi64>>,

        "input_batch_dimension",
        BuildVhloIntV1Attr(op->input_batch_dimension, builder)));
    attributes.emplace_back(builder.getNamedAttr(
        "input_feature_dimension",
        BuildVhloIntV1Attr(op->input_feature_dimension, builder)));
    attributes.emplace_back(builder.getNamedAttr(
        "kernel_input_feature_dimension",
        BuildVhloIntV1Attr(op->kernel_input_feature_dimension, builder)));

  tensorflow::UniformQuantizedConvolutionDimensionNumbersAttr dimension_numbers;
  if (!tensorflow::protobuf::TextFormat::ParseFromString(
          R"pb(
            input_batch_dimension: 0
            input_feature_dimension: 3
            input_spatial_dimensions: 1
            input_spatial_dimensions: 2
            kernel_output_feature_dimension: 3
            kernel_input_feature_dimension: 2

        &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()) {
    dnums.add_input_spatial_dimensions(value);
  }
  dnums.set_kernel_input_feature_dimension(

table StablehloConvolutionOptions{
  window_strides : [long];
  padding : [long];
  lhs_dilation : [long];
  rhs_dilation : [long];
  window_reversal : [bool];
  input_batch_dimension : long;
  input_feature_dimension : long;
  input_spatial_dimensions : [long];
  kernel_input_feature_dimension : long;
  kernel_output_feature_dimension : long;
  kernel_spatial_dimensions : [long];
  output_batch_dimension : long;