def __call__(self,
                 *args,
                 **kwargs):
        """
        Process probabilities to labels
        Args:
            Every argument is a list of vectors with probability distribution
        Returns:
            list of labels (only label classification) or list of lists of labels (multi-label classification),
            or list of the following lists (in multitask setting) for every argument
        """
        answer = []
        log.debug(f'input {args}')
        for data in args:
            if all([k is None for k in data]):
                answer.append([])
            elif self.confidence_threshold:
                if self.is_binary:
                    answer.append([int(el > self.confidence_threshold) for el in data])
                else:
                    answer.append([list(np.where(np.array(d) > self.confidence_threshold)[0]) for d in data])
            elif self.max_proba:
                answer.append([np.argmax(d) for d in data])
            elif self.top_n:
                answer.append([np.argsort(d)[::-1][:self.top_n] for d in data])
            else:
                raise ConfigError("Proba2Labels requires one of three arguments: bool `max_proba` or "
                                  "float `confidence_threshold` for multi-label classification or"
                                  "integer `top_n` for choosing several labels with the highest probabilities")
        if len(args) == 1:  # only one argument
            answer = answer[0]
        log.debug(f'output {answer}')
        return answer

        Process probabilities to labels
        Args:
            Every argument is a list of vectors with probability distribution
        Returns:
            list of labels (only label classification) or list of lists of labels (multi-label classification),
            or list of the following lists (in multitask setting) for every argument
        
deeppavlov/models/classifiers/proba2labels.py