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
code: def __init__(
      self,
      est,
      parama,
      est2=None,
      paramb="default",
      paramc=None,
      fmt="dense",
      labels="indicator",
  ):
      # estimators should precede parameters
      #  if estimators have default values, set None and initialize below
  
      # todo: write any hyper-parameters and components to self
      self.est = est
      self.parama = parama
      self.paramb = paramb
      self.paramc = paramc
  
      # leave this as is
      super().__init__(fmt=fmt, labels=labels)
  
file_path: sktime/extension_templates/annotation.py
imports: from sktime.annotation.base import BaseSeriesAnnotator
label_desc: Use image node_id 1 to create a new MySeriesAnnotator object from inherited base classes: BaseSeriesAnnotator with example: obj = MySeriesAnnotator(est, parama, est2, paramb, paramc, fmt, labels)