docstr: Train the transformer, and tranform the time series used for
  training.
  
  Parameters
  ----------
  df: pandas.DataFrame
      Time series to be used for training and be transformed.
  
  Returns
  -------
  pandas.Series or pandas.DataFrame
      Transformed time series.
docstr_tok: ["Train","the","transformer",",","and","tranform","the","time","series","used","for","training",".","Parameters","--","--","--","--","--","df",":","pandas.DataFrame","Time","series","to","be","used","for","training","and","be","transformed",".","Returns","--","--","--","-","pandas.Series","or","pandas.DataFrame","Transformed","time","series","."]
code: def fit_predict(
      self, df: pd.DataFrame
  ) -> Union[pd.Series, pd.DataFrame]:
      """Train the transformer, and tranform the time series used for
      training.
  
      Parameters
      ----------
      df: pandas.DataFrame
          Time series to be used for training and be transformed.
  
      Returns
      -------
      pandas.Series or pandas.DataFrame
          Transformed time series.
  
      """
      self.fit(df)
      return self.predict(df)
  
file_path: adtk/src/adtk/_transformer_base.py
imports: from typing import Union
  import pandas
  from ._base import _NonTrainableMultivariateModel, _NonTrainableUnivariateModel, _TrainableMultivariateModel, _TrainableUnivariateModel
label_desc: Use image node_id 3 for calling the _TrainableMultivariateTransformer obj's underlying member method code with example usage: obj.fit_predict(df) and returns: self