docstr: Aggregate multiple lists of anomalies into one.
  
  Parameters
  ----------
  lists: pandas.DataFrame, a dict of Series/DataFrame, or a dict of lists
      Anomaly lists to be aggregated.
  
      - If a pandas DataFrame, every column is a binary Series
        representing a type of anomaly.
      - If a dict of pandas Series/DataFrame, every value of the dict is
        a binary Series/DataFrame representing a type or some types of
        anomaly;
      - If a dict of list, every value of the dict is a type of anomaly
        as a list of events, where each event is represented as a pandas
        Timestamp if it is instantaneous or a 2-tuple of pandas
        Timestamps if it is a closed time interval.
  
  Returns
  -------
  list or a binary pandas Series
      Aggregated list of anomalies.
  
      - If input is a pandas DataFrame or a dict of Series/DataFrame,
        return a single binary pandas Series;
      - If input is a dict of lists, return a single list of events.
docstr_tok: ["Aggregate","multiple","lists","of","anomalies","into","one",".","Parameters","--","--","--","--","--","lists",":","pandas.DataFrame",",","a","dict","of","Series\/DataFrame",",","or","a","dict","of","lists","Anomaly","lists","to","be","aggregated",".","-","If","a","pandas","DataFrame",",","every","column","is","a","binary","Series","representing","a","type","of","anomaly",".","-","If","a","dict","of","pandas","Series\/DataFrame",",","every","value","of","the","dict","is","a","binary","Series\/DataFrame","representing","a","type","or","some","types","of","anomaly",";","-","If","a","dict","of","list",",","every","value","of","the","dict","is","a","type","of","anomaly","as","a","list","of","events",",","where","each","event","is","represented","as","a","pandas","Timestamp","if","it","is","instantaneous","or","a","2-tuple","of","pandas","Timestamps","if","it","is","a","closed","time","interval",".","Returns","--","--","--","-","list","or","a","binary","pandas","Series","Aggregated","list","of","anomalies",".","-","If","input","is","a","pandas","DataFrame","or","a","dict","of","Series\/DataFrame",",","return","a","single","binary","pandas","Series",";","-","If","input","is","a","dict","of","lists",",","return","a","single","list","of","events","."]
code: def predict(
      self,
      lists: Union[
          pd.DataFrame,
          Dict[str, Union[pd.Series, pd.DataFrame]],
          Dict[
              str,
              List[
                  Union[Tuple[pd.Timestamp, pd.Timestamp], pd.Timestamp]
              ],
          ],
      ],
  ) -> Union[
      pd.Series,
      List[Union[Tuple[pd.Timestamp, pd.Timestamp], pd.Timestamp]],
  ]:
      """Aggregate multiple lists of anomalies into one.
  
      Parameters
      ----------
      lists: pandas.DataFrame, a dict of Series/DataFrame, or a dict of lists
          Anomaly lists to be aggregated.
  
          - If a pandas DataFrame, every column is a binary Series
            representing a type of anomaly.
          - If a dict of pandas Series/DataFrame, every value of the dict is
            a binary Series/DataFrame representing a type or some types of
            anomaly;
          - If a dict of list, every value of the dict is a type of anomaly
            as a list of events, where each event is represented as a pandas
            Timestamp if it is instantaneous or a 2-tuple of pandas
            Timestamps if it is a closed time interval.
  
      Returns
      -------
      list or a binary pandas Series
          Aggregated list of anomalies.
  
          - If input is a pandas DataFrame or a dict of Series/DataFrame,
            return a single binary pandas Series;
          - If input is a dict of lists, return a single list of events.
  
      """
      return self._predict(lists)
  
imports: from typing import Dict, List, Tuple, Union
  import pandas
  from ._base import _NonTrainableModel
label_desc: Use image node_id 2 for calling the _Aggregator obj's underlying member method code with example usage: obj.predict(lists) and returns: self