return {callee};
  }

  CallTargetListTy result;
  for (const auto& name_attr_pair : n.attrs()) {
    const AttrValue& attr_value = name_attr_pair.second;
    if (attr_value.value_case() == AttrValue::kFunc) {
      result.push_back(attr_value.func());
    } else if (attr_value.value_case() == AttrValue::kList) {
      result.insert(result.end(), attr_value.list().func().begin(),
                    attr_value.list().func().end());

          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {
    llvm::ArrayRef<mlir::TensorType> subtypes = resource_type.getSubtypes();
    if (!subtypes.empty()) {
      AttrValue handle_dtypes_attr;
      AttrValue handle_shapes_attr;
      for (mlir::TensorType subtype : subtypes) {
        DataType dtype;
        TF_RETURN_IF_ERROR(ConvertToDataType(subtype.getElementType(), &dtype));

          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {
    llvm::ArrayRef<mlir::TensorType> subtypes = resource_type.getSubtypes();
    if (!subtypes.empty()) {
      AttrValue handle_dtypes_attr;
      AttrValue handle_shapes_attr;
      for (mlir::TensorType subtype : subtypes) {
        DataType dtype;
        TF_RETURN_IF_ERROR(ConvertToDataType(subtype.getElementType(), &dtype));

              if (attr.has_default_value() &&
                  attr.default_value().value_case() == AttrValue::kType) {
                type = DataType_Name(attr.default_value().type());
              } else if (attr.has_allowed_values() &&
                         attr.allowed_values().value_case() ==
                             AttrValue::kList &&
                         !attr.allowed_values().list().type().empty()) {

#include "tensorflow/core/graph/graph_def_builder.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

AttrValue FuncListAttr(const absl::Span<const char* const> names) {
  AttrValue attr;
  for (const char* name : names) {
    attr.mutable_list()->add_func()->set_name(name);
  }
  return attr;
}

constexpr char kFunctionalIfNodeName[] = "If";

                                  const void* proto, size_t proto_len,
                                  TF_Status* status) {
  tensorflow::AttrValue attr_value;
  if (!attr_value.ParseFromArray(proto, proto_len)) {
    status->status = InvalidArgument(
        "Unparseable AttrValue proto passed to "
        "TF_FunctionSetAttrValueProto");
    return;
  }

  auto fdef_or = func->record->mutable_fdef();

        }
        return new HashMap<String, Object>();
    }

    @Override
    protected void addChildUrlFromTagAttribute(final List<String> urlList, final URL url, final String attrValue, final String encoding) {
        final String urlValue = attrValue.trim();
        URL childUrl;
        String u = null;
        try {
            childUrl = new URL(url, urlValue.startsWith(":") ? url.getProtocol() + urlValue : urlValue);

    }

    OpBuilder builder(loc.getContext());
    for (const auto& name_and_value : node_def.attr()) {
      const std::string& attr_name = name_and_value.first;
      const tensorflow::AttrValue& attr_value = name_and_value.second;
      absl::StatusOr<Attribute> mlir_attr =
          tensorflow::ConvertAttributeValue(attr_value, &builder);
      if (!mlir_attr.ok()) {

static Status ValidateNode(const NodeDef& node) {
  const auto node_iterator = node.attr().find("value");
  if (node_iterator != node.attr().end()) {
    AttrValue node_value = node_iterator->second;
    if (node_value.has_tensor()) {
      const PartialTensorShape node_shape(node_value.tensor().tensor_shape());
      if (node_shape.num_elements() < 0) {

  TF_DeleteBuffer(buffer);
  TF_DeleteStatus(s);
  return ret;
}

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s) {
  TF_Buffer* buffer = TF_NewBuffer();
  TF_OperationGetAttrValueProto(oper, attr_name, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;