This method can be used for extending already generated vocabulary.
        It takes same parameters as Vocabulary initializer. The token2index
        and indextotoken mappings of calling vocabulary will be retained.
        It is an inplace operation so None will be returned.

def _extend(self,
                counter: Dict[str, Dict[str, int]] = None,
                min_count: Dict[str, int] = None,
                max_vocab_size: Union[int, Dict[str, int]] = None,
                non_padded_namespaces: Iterable[str] = DEFAULT_NON_PADDED_NAMESPACES,
                pretrained_files: Optional[Dict[str, str]] = None,
                only_include_pretrained_words: bool = False,
                tokens_to_add: Dict[str, List[str]] = None,
                min_pretrained_embeddings: Dict[str, int] = None) -> None:
        """
        This method can be used for extending already generated vocabulary.
        It takes same parameters as Vocabulary initializer. The token2index
        and indextotoken mappings of calling vocabulary will be retained.
        It is an inplace operation so None will be returned.
        """
        if not isinstance(max_vocab_size, dict):
            int_max_vocab_size = max_vocab_size
            max_vocab_size = defaultdict(lambda: int_max_vocab_size)  # type: ignore
        min_count = min_count or {}
        pretrained_files = pretrained_files or {}
        min_pretrained_embeddings = min_pretrained_embeddings or {}
        non_padded_namespaces = set(non_padded_namespaces)
        counter = counter or {}
        tokens_to_add = tokens_to_add or {}

        self._retained_counter = counter
        # Make sure vocabulary extension is safe.
        current_namespaces = {*self._token_to_index}
        extension_namespaces = {*counter, *tokens_to_add}

        for namespace in current_namespaces & extension_namespaces:
            # if new namespace was already present
            # Either both should be padded or none should be.
            original_padded = not any(namespace_match(pattern, namespace)
                                      for pattern in self._non_padded_namespaces)
            extension_padded = not any(namespace_match(pattern, namespace)
                                       for pattern in non_padded_namespaces)
            if original_padded != extension_padded:
                raise ConfigurationError("Common namespace {} has conflicting ".format(namespace)+
                                         "setting of padded = True/False. "+
                                         "Hence extension cannot be done.")

        # Add new non-padded namespaces for extension
        self._token_to_index.add_non_padded_namespaces(non_padded_namespaces)
        self._index_to_token.add_non_padded_namespaces(non_padded_namespaces)
        self._non_padded_namespaces.update(non_padded_namespaces)

        for namespace in counter:
            if namespace in pretrained_files:
                pretrained_list = _read_pretrained_tokens(pretrained_files[namespace])
                min_embeddings = min_pretrained_embeddings.get(namespace, 0)
                if min_embeddings > 0:
                    tokens_old = tokens_to_add.get(namespace, [])
                    tokens_new = pretrained_list[:min_embeddings]
                    tokens_to_add[namespace] = tokens_old + tokens_new
                pretrained_set = set(pretrained_list)
            else:
                pretrained_set = None
            token_counts = list(counter[namespace].items())
            token_counts.sort(key=lambda x: x[1], reverse=True)
            try:
                max_vocab = max_vocab_size[namespace]
            except KeyError:
                max_vocab = None
            if max_vocab:
                token_counts = token_counts[:max_vocab]
            for token, count in token_counts:
                if pretrained_set is not None:
                    if only_include_pretrained_words:
                        if token in pretrained_set and count >= min_count.get(namespace, 1):
                            self.add_token_to_namespace(token, namespace)
                    elif token in pretrained_set or count >= min_count.get(namespace, 1):
                        self.add_token_to_namespace(token, namespace)
                elif count >= min_count.get(namespace, 1):
                    self.add_token_to_namespace(token, namespace)

        for namespace, tokens in tokens_to_add.items():
            for token in tokens:
                self.add_token_to_namespace(token, namespace)