.getDimSize(input_feature_dimension);
    const int kernel_input_feature_dimension =
        dnums.getKernelInputFeatureDimension();
    const int kernel_input_channels =
        mlir::cast<ShapedType>(conv_op.getRhs().getType())
            .getDimSize(kernel_input_feature_dimension);
    const int64_t feature_group_count = conv_op.getFeatureGroupCount();

                                           vhlo_op.getInputFeatureDimension())
                                           .getValue()
                                           .getSExtValue();
        auto kernel_input_feature_dimension =
            mlir::cast<mlir::vhlo::IntegerV1Attr>(
                vhlo_op.getKernelInputFeatureDimension())
                .getValue()
                .getSExtValue();

  auto input_features = input_tensor_shape[dnums.input_feature_dimension()];
  auto input_batch = input_tensor_shape[dnums.input_batch_dimension()];
  auto kernel_input_features =
      kernel_tensor_shape[dnums.kernel_input_feature_dimension()];
  auto kernel_output_features =
      kernel_tensor_shape[dnums.kernel_output_feature_dimension()];

  if (feature_group_count_val <= 0) {
    return op.emitOpError()