docstr: Train the transformer with given time series.
  
  Parameters
  ----------
  ts: pandas.Series or pandas.DataFrame
      Time series to be used to train the transformer.
      If a DataFrame with k columns, k univariate transformers will be
      trained independently.
docstr_tok: ["Train","the","transformer","with","given","time","series",".","Parameters","--","--","--","--","--","ts",":","pandas.Series","or","pandas.DataFrame","Time","series","to","be","used","to","train","the","transformer",".","If","a","DataFrame","with","k","columns",",","k","univariate","transformers","will","be","trained","independently","."]
code: def fit(self, ts: Union[pd.Series, pd.DataFrame]) -> None:
      """Train the transformer with given time series.
  
      Parameters
      ----------
      ts: pandas.Series or pandas.DataFrame
          Time series to be used to train the transformer.
          If a DataFrame with k columns, k univariate transformers will be
          trained independently.
  
      """
      self._fit(ts)
  
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 1 for calling the _TrainableUnivariateTransformer obj's underlying member method code with example usage: obj.fit(ts) without return types