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 internal memory of seen training data.
  
  Accesses in self:
  _X : only if exists, then assumed same type as X and same cols
      these assumptions should be guaranteed by calls
  
  Writes to self:
  _X : same type as X - new rows from X are added to current _X
      if _X does not exist, stores X as _X
  
  _X is guaranteed to be one of mtypes:
      pd.DataFrame, pd.Series, np.ndarray, pd-multiindex, numpy3D,
      pd_multiindex_hier
  
  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
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code: def _update_X(self, X):
      """Update internal memory of seen training data.
  
      Accesses in self:
      _X : only if exists, then assumed same type as X and same cols
          these assumptions should be guaranteed by calls
  
      Writes to self:
      _X : same type as X - new rows from X are added to current _X
          if _X does not exist, stores X as _X
  
      _X is guaranteed to be one of mtypes:
          pd.DataFrame, pd.Series, np.ndarray, pd-multiindex, numpy3D,
          pd_multiindex_hier
  
      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
      """
      if X is not None:
          # unwrap X if VectorizedDF
          if isinstance(X, VectorizedDF):
              X = X.X_multiindex
          # if _X does not exist yet, initialize it with X
          if (
              not hasattr(self, "_X")
              or self._X is None
              or not self.is_fitted
          ):
              self._X = X
          else:
              self._X = update_data(self._X, X)
  
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 6 for calling the BaseParamFitter obj's underlying member method code with example usage: obj._update_X(X) without return types