"lhs_dilation", BuildVhloTensorV1Attr(shape, data, builder)));
    }
    if (!(op->rhs_dilation.empty())) {
      std::vector<int64_t> shape;
      shape.push_back(static_cast<int64_t>(op->rhs_dilation.size()));
      attributes.emplace_back(builder.getNamedAttr(
          "rhs_dilation",
          BuildVhloTensorV1Attr(shape, op->rhs_dilation, builder)));
    } else {

    dimension_numbers = #mhlo.conv<[b, 0, f]x[0, i, o]->[b, 0, f]>,
    feature_group_count = 1 : i64,
    lhs_dilation = dense<1> : tensor<1xi64>,
    padding = dense<0> : tensor<1x2xi64>,
    precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>],
    rhs_dilation = dense<1> : tensor<1xi64>,
    window_strides = dense<1> : tensor<1xi64>

    } 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);
  }

        }
      }
      paddings.push_back(pad_low);
      paddings.push_back(pad_high);
    }
    auto rhs_dilations_attr = rewriter.getNamedAttr(
        "rhs_dilation", GetI64ElementsAttr(rhs_dilations, &rewriter));

    auto window_strides_attr = rewriter.getNamedAttr(
        "window_strides", GetI64ElementsAttr(window_strides, &rewriter));

  { auto _e = lhs_dilation(); if (_e) { _o->lhs_dilation.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_dilation[_i] = _e->Get(_i); } } else { _o->lhs_dilation.resize(0); } }
  { auto _e = rhs_dilation(); if (_e) { _o->rhs_dilation.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_dilation[_i] = _e->Get(_i); } } else { _o->rhs_dilation.resize(0); } }

    StrAttr:$padding,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$explicit_padding,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$lhs_dilation,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$rhs_dilation,
    DefaultValuedOptionalAttr<I64Attr, "1">:$batch_group_count,
    DefaultValuedOptionalAttr<I64Attr, "1">:$feature_group_count,

            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()),

  %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,...