bool MatchInputDimensionNumbers(
      const ConvDimensionNumbersAttr dimension_numbers) const {
    return dimension_numbers.getInputBatchDimension() == 0 &&
           dimension_numbers.getInputFeatureDimension() == 1 &&
           dimension_numbers.getInputSpatialDimensions() ==
               ArrayRef<int64_t>{2, 3};
  }

  // Matches kernel dimensions corresponding to: [o, i, 0, 1].

      dilation.emplace_back(1);
    }

    mhlo::ConvDimensionNumbersAttr dnums = conv_op.getDimensionNumbers();
    const int input_feature_dimension = dnums.getInputFeatureDimension();
    const int kernel_input_feature_dimension =
        dnums.getKernelInputFeatureDimension();
    const int input_channels =
        mlir::cast<ShapedType>(conv_op.getLhs().getType())

      // equal to input channel * `depth_multiplier`.
      const int64_t multiplier = dimension_numbers.getOutputFeatureDimension() /
                                 dimension_numbers.getInputFeatureDimension();

      rewriter.replaceOpWithNewOp<TFL::DepthwiseConv2DOp>(
          // op result should be recasted to desired quantized type.
          op, output_type,
          /*input=*/input_value,

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