Value new_conv = rewriter.create<mhlo::ConvolutionOp>(
        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(),

        op.getLoc(), /*resultType0=*/new_conv_output_tensor_type,
        /*lhs=*/input_transpose_op,
        /*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,

FailureOr<ElementsAttr> ConvertPaddingAttr(
    UniformQuantizedConvolutionOp op,
    const xla::ConvolutionDimensionNumbers &dnums, PatternRewriter &rewriter) {
  StringAttr conv_padding = op.getPaddingAttr();
  SmallVector<int64_t> padding_nums;
  ShapedType lhs_shape = mlir::cast<ShapedType>(op.getLhs().getType());
  ShapedType rhs_shape = mlir::cast<ShapedType>(op.getRhs().getType());

#include "tsl/platform/status.h"

namespace {

using ::absl::StatusOr;
using ::tensorflow::Status;
using ::tensorflow::errors::InvalidArgument;

StatusOr<mlir::StringAttr> GetPaddingAttr(TfLitePadding pad_params,
                                          mlir::Builder builder,
                                          mlir::Location loc) {
  auto padding = tflite::Padding::Padding_VALID;

        /*fuse_bias_constant=*/fuse_bias_constant);

    // Determine the attributes for the TFL::Conv2DOp.
    Value input_value = op.getOperand(0);
    if (const DenseIntElementsAttr padding_attr = op.getPaddingAttr();
        !HasProperPadding(op, dimension_numbers, padding_attr)) {
      // Add an extra tfl.pad_op if there are explicit padding values. This
      // extra pad op will allow us to always set the `padding` attribute of

      auto conv_result = rewriter.create<mhlo::ConvolutionOp>(
          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());