class_docstr: Base parameter fitting estimator class.
  
  The base parameter fitter specifies the methods and method signatures that all
  parameter fitter have to implement.
  
  Specific implementations of these methods is deferred to concrete instances.
docstr: Update fitted parameters on more data.
  
  If no estimator-specific update method has been implemented,
  default fall-back is fitting to all observed data so far
  
  State required:
      Requires state to be "fitted".
  
  Accesses in self:
      Fitted model attributes ending in "_".
      Pointers to seen data, self._X
      self._is_fitted
      model attributes ending in "_".
  
  Writes to self:
      Update self._X with `X`, by appending rows.
      Updates fitted model attributes ending in "_".
  
  Parameters
  ----------
  X : time series in sktime compatible data container format
          Time series to which to fit the forecaster in the update.
      y can be in one of the following formats, must be same scitype as in fit:
      Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
      Panel scitype: pd.DataFrame with 2-level row MultiIndex,
          3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame
      Hierarchical scitype: pd.DataFrame with 3 or more level row MultiIndex
      For further details:
          on usage, see forecasting tutorial examples/01_forecasting.ipynb
          on specification of formats, examples/AA_datatypes_and_datasets.ipynb
  
  Returns
  -------
  self : reference to self
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code: def update(self, X):
      """Update fitted parameters on more data.
  
      If no estimator-specific update method has been implemented,
      default fall-back is fitting to all observed data so far
  
      State required:
          Requires state to be "fitted".
  
      Accesses in self:
          Fitted model attributes ending in "_".
          Pointers to seen data, self._X
          self._is_fitted
          model attributes ending in "_".
  
      Writes to self:
          Update self._X with `X`, by appending rows.
          Updates fitted model attributes ending in "_".
  
      Parameters
      ----------
      X : time series in sktime compatible data container format
              Time series to which to fit the forecaster in the update.
          y can be in one of the following formats, must be same scitype as in fit:
          Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
          Panel scitype: pd.DataFrame with 2-level row MultiIndex,
              3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame
          Hierarchical scitype: pd.DataFrame with 3 or more level row MultiIndex
          For further details:
              on usage, see forecasting tutorial examples/01_forecasting.ipynb
              on specification of formats, examples/AA_datatypes_and_datasets.ipynb
  
      Returns
      -------
      self : reference to self
      """
      self.check_is_fitted()
  
      if X is None or (hasattr(X, "__len__") and len(X) == 0):
          warn(
              f"empty y passed to update of {self}, no update was carried out",
              obj=self,
          )
          return self
  
      # input checks and minor coercions on X, y
      X_inner = self._check_X(X=X)
  
      # update internal X with the new X
      self._update_X(X_inner)
  
      # checks and conversions complete, pass to inner update
      self._update(X=X_inner)
  
      return self
  
file_path: sktime/sktime/param_est/base.py
imports: from sktime.base import BaseEstimator
  from sktime.datatypes import VectorizedDF, check_is_scitype, convert, scitype_to_mtype, update_data
  from sktime.utils.sklearn import is_sklearn_transformer
  from sktime.utils.validation._dependencies import _check_estimator_deps
  from sktime.utils.warnings import warn
label_desc: Use image node_id 4 for calling the BaseParamFitter obj's underlying member method code with example usage: obj.update(X) and returns: self, self