@overrides<EOL><INDENT>def __call__(self, batch: List[List[str]], tags_batch: Optional[List[List[str]]] = None, mean: bool = None,<EOL>*args, **kwargs) -> List[Union[list, np.ndarray]]:<DEDENT>
if self.tags_vocab:<EOL><INDENT>if tags_batch is None:<EOL><INDENT>raise ConfigError("<STR_LIT>")<EOL><DEDENT>batch = [self._tags_encode(sample, tags_sample, mean=mean) for sample, tags_sample in zip(batch, tags_batch)]<EOL><DEDENT>else:<EOL><INDENT>if tags_batch:<EOL><INDENT>raise ConfigError("<STR_LIT>")<EOL><DEDENT>batch = [self._encode(sample, mean=mean) for sample in batch]<EOL><DEDENT>if self.pad_zero:<EOL><INDENT>batch = zero_pad(batch)<EOL><DEDENT>return batch<EOL>
Infer on the given data

Args:
    batch: tokenized text samples
    tags_batch: optional batch of corresponding tags
    mean: whether to return mean token embedding (does not depend on self.mean)
    *args: additional arguments
    **kwargs: additional arguments

Returns: