list->add_s(s);
  }
  return {string_list_attr.first, attr_value};
}

impl::NodeMatcherProperties impl::Attr(std::pair<string, AttrValue> attr) {
  impl::NodeMatcherProperties props;
  props.set_attr(std::move(attr));
  return props;
}

impl::NodeMatcherProperties impl::Attr(string name) {
  impl::NodeMatcherProperties props;
  props.set_attr({std::move(name), std::nullopt});
  return props;
}

    it->second.second.emplace_back(std::move(node_info));
  }
}

// Returns `true` iff node has a given `attr` set to `true`. Returns `false`
// both for the missing attr, and the attr set to `false`.
static bool HasBoolAttr(const NodeDef& node, const char* attr) {
  const auto& it = node.attr().find(attr);
  return it != node.attr().end() && it->second.b();
}

bool CanCreateXlaKernel(const NodeDef& node_def) {

            Map<String, String> attrs = dominant.getAttributes();
            List<XmlNode> children = null;

            for (Map.Entry<String, String> attr : recessive.getAttributes().entrySet()) {
                String key = attr.getKey();
                if (isEmpty(attrs.get(key))) {
                    if (attrs == dominant.getAttributes()) {
                        attrs = new HashMap<>(attrs);
                    }

    llvm::SmallVectorImpl<mlir::NamedAttribute> &output) {
  for (mlir::NamedAttribute attr : attrs) {
    if (attr.getName().strref().contains(
            mlir::tfg::TFGraphDialect::getDeviceAttrKey())) {
      tensorflow::DeviceNameUtils::ParsedName parsed_name;
      if (!tensorflow::DeviceNameUtils::ParseFullName(
              mlir::cast<mlir::StringAttr>(attr.getValue()).getValue().str(),

    }

    def "can serialize/deserialize isolated Attribute values"() {
        Attribute attr1 = Attribute.of("foo", String.class)
        Attribute attr2 = Attribute.of("bar", SomeType.class)
        Isolatable<?>[] isolatables = [isolatableFactory.isolate(attr1), isolatableFactory.isolate(attr2)]

        when:
        serialize(isolatables)

        and:

	BFalse bool    `xml:",attr"`
	BTrue  bool    `xml:",attr"`
	I      int     `xml:",attr"`
	INeg   int     `xml:",attr"`
	I8     int8    `xml:",attr"`
	I8Neg  int8    `xml:",attr"`
	I16    int16   `xml:",attr"`
	I16Neg int16   `xml:",attr"`
	I32    int32   `xml:",attr"`
	I32Neg int32   `xml:",attr"`
	I64    int64   `xml:",attr"`
	I64Neg int64   `xml:",attr"`
	UI     uint    `xml:",attr"`

}

Status ConvertAttribute(const mlir::BoolAttr& attr, AttrValue* value) {
  value->set_b(attr.getValue());
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::IntegerAttr& attr, AttrValue* value) {
  value->set_i(attr.getInt());
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::FloatAttr& attr, AttrValue* value) {
  value->set_f(attr.getValueAsDouble());
  return absl::OkStatus();
}

    op_state.addOperands(op.getOperands());
    op_state.addTypes(op.getResultTypes());

    SmallVector<NamedAttribute, 2> attrs;
    std::string parsed_op_name;
    tensorflow::NodeDef node_def;
    if (failed(ParseCustomOption(op.getCustomOption().getValue(), op.getLoc(),
                                 parsed_op_name, attrs, node_def))) {
      return failure();
    }
    if (parsed_op_name != tf_op_name) {
      return op.emitOpError(

    std::vector<NamedAttribute> attrs = contraction->getAttrs();
    ArrayAttr fused_ops_attr = ArrayAttr::get(context, fused_ops);
    attrs.push_back(
        NamedAttribute(StringAttr::get(context, "fused_ops"), fused_ops_attr));
    // Epsilon is used only in fusions with the FusedBatchNorm op, so we zero it
    // here.
    Attribute epsilon = rewriter.getF32FloatAttr(0);
    attrs.push_back(

              value.add_int64_val(dim.size());
            }
          }
          AddNodeAttr("value", value, &const_def);
          for (auto const& attr : n->attrs()) {
            if (*attr.first.begin() == '_') {
              AddNodeAttr(attr.first, attr.second, &const_def);
            }
          }

          TF_ASSIGN_OR_RETURN(Node * const_node, graph->AddNode(const_def));