(NamedAttributeList
      (NamedAttr<"window_strides"> (DefaultOrNullAttr $window_strides)),
      (NamedAttr<"padding"> (DefaultOrNullAttr $padding)),
      (NamedAttr<"lhs_dilation"> (DefaultOrNullAttr $lhs_dilation)),
      (NamedAttr<"rhs_dilation"> (DefaultOrNullAttr $rhs_dilation)),
      (NamedAttr<"window_reversal"> (DefaultOrNullAttr $window_reversal)),
      (NamedAttr<"dimension_numbers"> $dimension_numbers),

      (NamedAttr<"window_strides"> (DefaultOrNullAttr $window_strides)),
      (NamedAttr<"padding"> (DefaultOrNullAttr $padding)),
      (NamedAttr<"lhs_dilation"> (DefaultOrNullAttr $lhs_dilation)),
      (NamedAttr<"rhs_dilation"> (DefaultOrNullAttr $rhs_dilation)),
      (NamedAttr<"window_reversal"> (DefaultOrNullAttr $window_reversal)),
      (NamedAttr<"dimension_numbers"> $dimension_numbers),

    const TensorType new_conv_output_tensor_type =
        GetTransposedTensorType(output_tensor_type, kNchwToNhwcPermutation);

    // window_strides, padding, lhs_dilation, rhs_dilation, window_reversal are
    // reused without modification because the ordering of spatial dimensions
    // is not modified (i.e. before: [b, f, 0, 1], after: [b, 0, 1, f] => the

// CHECK-SAME: dimension_numbers = "\10\03\1A\02\01\02 \02(\032\02\00\01@\03J\02\01\02"
// CHECK-SAME: explicit_padding = []
// CHECK-SAME: feature_group_count = 1 : i64
// CHECK-SAME: lhs_dilation = [1, 1]
// CHECK-SAME: padding = "VALID"
// CHECK-SAME: rhs_dilation = [2, 2]
// CHECK-SAME: rhs_quantization_axis = -1 : i64
// CHECK-SAME: rhs_quantization_max_val = 127 : i64
// CHECK-SAME: rhs_quantization_min_val = -128 : i64

    %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>

          "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 {

  }

  Value padding, window_strides, lhs_dilation, rhs_dilation,
      feature_group_count;
  PrepareXlaConvParams(builder, loc, strides, dilations, feature_group_cnt,
                       /*window_strides=*/window_strides,
                       /*lhs_dilation=*/lhs_dilation,
                       /*rhs_dilation=*/rhs_dilation,
                       /*feature_group_count=*/feature_group_count,

      // $broadcast_dims
      CompileTimeConstantOperand<TF::XlaBroadcastHelperOp, 2>,
      // $window_strides, $padding, $lhs_dilation, $rhs_dilation,
      // $feature_group_count
      CompileTimeConstantOperand<TF::XlaConvOp, 2, 3, 4, 5, 6>,
      // $window_strides, $padding, $lhs_dilation, $rhs_dilation,
      // $feature_group_count
      CompileTimeConstantOperand<TF::XlaConvV2Op, 2, 3, 4, 5, 6>,

    explicit_padding = [], feature_group_count = 1 : i64, lhs_dilation = [1, 1],
    lhs_quantization_axis = -1 : i64, lhs_quantization_max_val = 127 : i64,
    lhs_quantization_min_val = -128 : i64, output_quantization_axis = -1 : i64,
    output_quantization_max_val = 2147483647 : i64,
    output_quantization_min_val = -2147483648 : i64, padding = "SAME",
    rhs_dilation = [1, 1], rhs_quantization_axis = -1 : i64,

          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 ||