def __call__(self, batch: List[List[int]], **kwargs) -> Union[List[List[np.ndarray]], List[np.ndarray]]:
        """
        Convert given batch of list of labels to one-hot representation of the batch.

        Args:
            batch: list of samples, where each sample is a list of integer labels.
            **kwargs: additional arguments

        Returns:
            if ``single_vector``, list of one-hot representations of each sample,
            otherwise, list of lists of one-hot representations of each label in a sample
        """
        one_hotted_batch = []

        for utt in batch:
            if isinstance(utt, Iterable):
                one_hotted_utt = self._to_one_hot(utt, self._depth)
            elif isinstance(utt, int):
                if self._pad_zeros or self.single_vector:
                    one_hotted_utt = self._to_one_hot([utt], self._depth)
                else:
                    one_hotted_utt = self._to_one_hot([utt], self._depth).reshape(-1)

            if self.single_vector:
                one_hotted_utt = np.sum(one_hotted_utt, axis=0)

            one_hotted_batch.append(one_hotted_utt)

        if self._pad_zeros:
            one_hotted_batch = zero_pad(one_hotted_batch)
        return one_hotted_batch

        Convert given batch of list of labels to one-hot representation of the batch.

        Args:
            batch: list of samples, where each sample is a list of integer labels.
            **kwargs: additional arguments

        Returns:
            if ``single_vector``, list of one-hot representations of each sample,
            otherwise, list of lists of one-hot representations of each label in a sample
        
deeppavlov/models/preprocessors/one_hotter.py