def METHOD_NAME(self, column_data: Iterable[Iterable]) -> torch.Tensor:
    """
    Encode the properties of a sequence-of-sequence representation
    :param column_data: Input column data to be encoded
    :returns: a torch-tensor representing the encoded sequence
    """
    if not self.is_prepared:
        raise Exception('You need to call "prepare" before calling "encode" or "decode".')
    if isinstance(column_data, pd.Series):
        column_data = column_data.values
    for i in range(len(column_data)):
        if is_none(column_data[i]):
            column_data[i] = [0] * self.output_size
    column_data = np.array([self._pad_and_strip(list(x)) for x in column_data])
    data = torch.cat([self._normalizer.METHOD_NAME(column_data)], dim=-1)
    data[torch.isnan(data)] = 0.0
    data[torch.isinf(data)] = 0.0
    return data