class_docstr: Custom series annotator.
  
  todo: write docstring, describing your custom forecaster
  
  Parameters
  ----------
  fmt : str {"dense", "sparse"}, optional (default="dense")
      Annotation output format:
      * If "sparse", a sub-series of labels for only the outliers in X is returned,
      * If "dense", a series of labels for all values in X is returned.
  labels : str {"indicator", "score"}, optional (default="indicator")
      Annotation output labels:
      * If "indicator", returned values are boolean, indicating whether a value is an
      outlier,
      * If "score", returned values are floats, giving the outlier score.
  
  parama : int
      descriptive explanation of parama
  paramb : string, optional (default='default')
      descriptive explanation of paramb
  paramc : boolean, optional (default= whether paramb is not the default)
      descriptive explanation of paramc
  and so on
  
  Components
  ----------
  est : sktime.estimator, BaseEstimator descendant
      descriptive explanation of est
  est2: another estimator
      descriptive explanation of est2
  and so on
docstr: Fit to training data.
  
  core logic
  
  Parameters
  ----------
  X : pd.DataFrame
      training data to fit model to, time series
  Y : pd.Series, optional
      ground truth annotations for training if annotator is supervised
  Returns
  -------
  self : returns a reference to self
  
  State change
  ------------
  creates fitted model (attributes ending in "_")
docstr_tok: ["Fit","to","training","data",".","core","logic","Parameters","--","--","--","--","--","X",":","pd.DataFrame","training","data","to","fit","model","to",",","time","series","Y",":","pd.Series",",","optional","ground","truth","annotations","for","training","if","annotator","is","supervised","Returns","--","--","--","-","self",":","returns","a","reference","to","self","State","change","--","--","--","--","--","--","creates","fitted","model","(","attributes","ending","in","``","_","''",")"]
code: def _fit(self, X, Y=None):
      """Fit to training data.
  
      core logic
  
      Parameters
      ----------
      X : pd.DataFrame
          training data to fit model to, time series
      Y : pd.Series, optional
          ground truth annotations for training if annotator is supervised
      Returns
      -------
      self : returns a reference to self
  
      State change
      ------------
      creates fitted model (attributes ending in "_")
      """
  
file_path: sktime/extension_templates/annotation.py
imports: from sktime.annotation.base import BaseSeriesAnnotator
label_desc: Use image node_id 2 for calling the MySeriesAnnotator obj's underlying member method code with example usage: obj._fit(X, Y) without return types