class_docstr: Base series annotator.
  
  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.
  
  Notes
  -----
  Assumes "predict" data is temporal future of "fit"
  Single time series in both, no meta-data.
  
  The base series annotator specifies the methods and method
  signatures that all annotators have to implement.
  
  Specific implementations of these methods is deferred to concrete
  annotators.
code: def __init__(self, fmt="dense", labels="indicator"):
      self.fmt = fmt
      self.labels = labels
  
      self._is_fitted = False
  
      self._X = None
      self._Y = None
  
      super().__init__()
  
file_path: sktime/sktime/annotation/base/_base.py
imports: from sktime.base import BaseEstimator
  from sktime.utils.validation.annotation import check_fmt, check_labels
  from sktime.utils.validation.series import check_series
label_desc: Use image node_id 1 to create a new BaseSeriesAnnotator object from inherited base classes: BaseEstimator with example: obj = BaseSeriesAnnotator(fmt, labels)