class_docstr: Delegator mixin that delegates all methods to wrapped transformer.
  
  Delegates inner transformer methods to a wrapped estimator.
      Wrapped estimator is value of attribute with name self._delegate_name.
      By default, this is "estimator_", i.e., delegates to self.estimator_
      To override delegation, override _delegate_name attribute in child class.
  
  Delegates the following inner underscore methods:
      _fit, _transform, _inverse_transform, _update
  
  Does NOT delegate get_params, set_params.
      get_params, set_params will hence use one additional nesting level by default.
  
  Does NOT delegate or copy tags, this should be done in a child class if required.
docstr: Update wrapped transformer with X, optionally y.
  
  State required:
      Requires state to be "fitted".
  
  Writes to self:
      May update fitted model attributes ending in for the delegated
      transformer "_" via update.
  
  Parameters
  ----------
  X : Series or Panel, any supported mtype
      Data to fit transform to, of python type as follows:
          Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
          Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
              nested pd.DataFrame, or pd.DataFrame in long/wide format
          subject to sktime mtype format specifications, for further details see
              examples/AA_datatypes_and_datasets.ipynb
  y : Series or Panel, default=None
      Additional data, e.g., labels for transformation
  update_params : bool, default=True
      whether the model is updated. Yes if true, if false, simply skips call.
      argument exists for compatibility with forecasting module.
  
  Returns
  -------
  self : a fitted instance of the estimator
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code: def _update(self, X, y=None, update_params=True):
      """Update wrapped transformer with X, optionally y.
  
      State required:
          Requires state to be "fitted".
  
      Writes to self:
          May update fitted model attributes ending in for the delegated
          transformer "_" via update.
  
      Parameters
      ----------
      X : Series or Panel, any supported mtype
          Data to fit transform to, of python type as follows:
              Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
              Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
                  nested pd.DataFrame, or pd.DataFrame in long/wide format
              subject to sktime mtype format specifications, for further details see
                  examples/AA_datatypes_and_datasets.ipynb
      y : Series or Panel, default=None
          Additional data, e.g., labels for transformation
      update_params : bool, default=True
          whether the model is updated. Yes if true, if false, simply skips call.
          argument exists for compatibility with forecasting module.
  
      Returns
      -------
      self : a fitted instance of the estimator
      """
      estimator = self._get_delegate()
      estimator.update(y=y, X=X, update_params=update_params)
      return self
  
file_path: sktime/sktime/transformations/_delegate.py
imports: from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 5 for calling the _DelegatedTransformer obj's underlying member method code with example usage: obj._update(X, y, update_params) and returns: self