%feature_group_count = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
    %lhs_dilation = "tf.Const"() {value = dense<[4, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
    %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>

          conv_op, "can only be converted to 2D resize op");

    auto lhs_dilation = conv_op.getLhsDilation().value();
    auto rhs_dilation = conv_op.getRhsDilation().value();
    auto window_strides = conv_op.getWindowStrides().value();
    auto padding = conv_op.getPadding().value();
    if (lhs_dilation.getNumElements() != 2 || !rhs_dilation.isSplat() ||
        rhs_dilation.getSplatValue<int64_t>() != 1 ||

    } else {
      dim->set_padding_low(0);
      dim->set_padding_high(0);
    }
    if (!lhs_dilation.empty()) {
      dim->set_base_dilation(lhs_dilation[i]);
    } else {
      dim->set_base_dilation(1);
    }
    if (!rhs_dilation.empty()) {
      dim->set_window_dilation(rhs_dilation[i]);
    } else {
      dim->set_window_dilation(1);
    }
    dim->set_window_reversal(false);
  }

  %feature_group_count = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %lhs_dilation = "tf.Const"() {value = dense<[4, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
  %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>

  if (lhs_dilation_attr.getType().getRank() != 1)
    return op.emitOpError() << "expects lhs_dilation to be a vecotr";

  if (rhs_dilation_attr.getType().getRank() != 1)
    return op.emitOpError() << "expects rhs_dilation to be a vecotr";

  if (feature_group_count_attr.getType().getRank())
    return op.emitOpError() << "expects feature_group_count to be a scalar";

  %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64, window_strides = array<i64: 1, 1>, lhs_dilation = array<i64: 3, 3>} : (tensor<1x5x5x2x!quant.uniform<i8:f32, 2.000000e+00:0>>, tensor<2x2x2x4x!quant.uniform<i8:f32:3, {3.000000e+00, 3.000000e+00, 3.000000e+00, 3.000000e+00}>>) -> tensor<1x14x14x4x!quant.uniform<i32:f32:3, {6.000000e+00, 6.000000e+00,...

            mlir::cast<mlir::vhlo::TensorV1Attr>(vhlo_op.getPadding()),
            vhlo_type_converter));
        auto lhs_dialation = builder_.CreateVector(mlir::GetVector<int64_t>(
            mlir::cast<mlir::vhlo::TensorV1Attr>(vhlo_op.getLhsDilation()),
            vhlo_type_converter));
        auto rhs_dialation = builder_.CreateVector(mlir::GetVector<int64_t>(
            mlir::cast<mlir::vhlo::TensorV1Attr>(vhlo_op.getRhsDilation()),