int64_t total_size;
} TF_AttrMetadata;

// Returns metadata about the value of the attribute `attr_name` of `oper`.
TF_CAPI_EXPORT extern TF_AttrMetadata TF_OperationGetAttrMetadata(
    TF_Operation* oper, const char* attr_name, TF_Status* status);

// Fills in `value` with the value of the attribute `attr_name`.  `value` must
// point to an array of length at least `max_length` (ideally set to

      op_name: Name of the op to match.
      attr_name: Name of the attribute of the op to match.
      attr_val: Value of the attr_name to check.
      node_name: Name of the node to match. Accepts regex2 format.

    Returns:
      True if there exists a node whose name matches `op_name` and 'attr_val' if
      'attr_name' is given.
    """

    def match_node_name(name):
      if not node_name:

// Helper macros for the TF_OpKernelConstruction_GetAttr* tests.
#define EXPECT_TF_SIZE(attr_name, expected_list_size, expected_total_size) \
  do {                                                                     \
    int32_t list_size, total_size;                                         \
    TF_OpKernelConstruction_GetAttrSize(ctx, attr_name, &list_size,        \
                                        &total_size, status);              \

      std::vector<std::string> attr_names = {
          TF::kReplicationInfoAttr.str(), "_xla_compile_device_type",
          kEmbeddingPipelining, "_xla_outside_compilation", "device"};
      for (const auto& attr_name : attr_names) {
        if (!global_iter_id_op->hasAttr(attr_name)) continue;
        offset->setAttr(attr_name, global_iter_id_op->getAttr(attr_name));
        new_global_iter_id->setAttr(attr_name,

    func_ = TF_FunctionImportFunctionDef(buf.data(), buf.size(), s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  void GetAttr(const char* attr_name, AttrValue* out_attr) {
    TF_Buffer* attr_buf = TF_NewBuffer();
    TF_FunctionGetAttrValueProto(func_, attr_name, attr_buf, s_);
    ASSERT_TRUE(out_attr->ParseFromArray(attr_buf->data, attr_buf->length));
    TF_DeleteBuffer(attr_buf);
  }

  return std::nullopt;
}

// Returns true iff the attribute `attr_name` is attached to either the node or
// to it's callee.
static bool GetNodeOrFuncAttr(Node* node, FunctionLibraryDefinition* flib_def,
                              const char* attr_name) {
  bool out = false;
  bool attr_value;
  if (TryGetNodeAttr(node->attrs(), attr_name, &attr_value)) {
    out |= attr_value;
  }

// That will require setting up the tensorflow build with gmock.
#define EXPECT_TF_META(attr_name, expected_list_size, expected_type, \
                       expected_total_size)                          \
  do {                                                               \
    auto m = TF_OperationGetAttrMetadata(oper, attr_name, s_);       \
    EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);              \

// function, this map (map1) is in {attr_name_1: attr_identifier} format; and in
// the float function, this map (map2) is in {attr_identifier: attr_name_2}
// format. Where, the attribute identifiers should match between two maps,
// attr_name_1 is the name of the of the attribute needs to be set in the
// quantized function, attr_name_2 is the name of the attribute corresponding to

					Children: true,
					Field: []dwarf.Field{
						{Attr: dwarf.AttrProducer, Val: "GNU C 4.4.1", Class: dwarf.ClassString},
						{Attr: dwarf.AttrLanguage, Val: int64(1), Class: dwarf.ClassConstant},
						{Attr: dwarf.AttrName, Val: "go-relocation-test.c", Class: dwarf.ClassString},
						{Attr: dwarf.AttrCompDir, Val: "/tmp", Class: dwarf.ClassString},
						{Attr: dwarf.AttrLowpc, Val: uint64(0x0), Class: dwarf.ClassAddress},

    if arg_def.type == types_pb2.DT_FLOAT:
      attr_names.append('f32_')
    elif arg_def.type == types_pb2.DT_INT32:
      attr_names.append('i32_')
    elif arg_def.type == types_pb2.DT_INT64:
      attr_names.append('i64_')
    elif arg_def.type == types_pb2.DT_BOOL:
      attr_names.append('i1_')

    if not attr_names:
      return str(attr_type)
    else: