mul_op.getLoc(), conv_op.getType(), conv_op.getLhs(), new_filter,
        conv_op.getWindowStridesAttr(), conv_op.getPaddingAttr(),
        conv_op.getLhsDilationAttr(), conv_op.getRhsDilationAttr(),
        conv_op.getWindowReversalAttr(), conv_op.getDimensionNumbers(),
        conv_op.getFeatureGroupCount(), conv_op.getBatchGroupCount(),
        conv_op.getPrecisionConfigAttr());

        /*rhs=*/filter_transpose_op,
        /*window_strides=*/op.getWindowStridesAttr(),
        /*padding=*/op.getPaddingAttr(),
        /*lhs_dilation=*/op.getLhsDilationAttr(),
        /*rhs_dilation=*/op.getRhsDilationAttr(),
        /*window_reversal=*/op.getWindowReversalAttr(),
        /*dimension_numbers=*/new_dimension_nums,
        /*feature_group_count=*/op.getFeatureGroupCountAttr(),

      const int64_t rhs_size_dilated =
          ::tensorflow::UniformQuantizedConvolutionParams::DilatedSize(
              rhs_shape.getDimSize(dnums.kernel_spatial_dimensions(i)),
              mlir::cast<IntegerAttr>(op.getRhsDilationAttr()[i]).getInt());

      const int64_t output_size = (lhs_size_dilated + stride - 1) / stride;
      const int64_t total_padding = std::max(
          (output_size - 1) * stride + rhs_size_dilated - lhs_size_dilated,

          conv_op.getLoc(), new_output_type, sliced_input, sliced_kernel,
          conv_op.getWindowStridesAttr(), conv_op.getPaddingAttr(),
          conv_op.getLhsDilationAttr(), conv_op.getRhsDilationAttr(),
          conv_op.getWindowReversalAttr(), conv_op.getDimensionNumbers(), 1, 1,
          conv_op.getPrecisionConfigAttr());

      conv_results.push_back(conv_result);
    }