class_docstr: Transformer that applies outlier detector from pyOD.
  
  Parameters
  ----------
  estimator : PyOD estimator
      See ``https://pyod.readthedocs.io/en/latest/`` documentation for a detailed
      description of all options.
  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.
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code: def __init__(self, estimator, fmt="dense", labels="indicator"):
      self.estimator = estimator  # pyod estimator
      super().__init__(fmt=fmt, labels=labels)
  
file_path: sktime/sktime/annotation/adapters/_pyod.py
imports: import numpy
  from sklearn.base import clone
  from sktime.annotation.base._base import BaseSeriesAnnotator
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  import pandas
label_desc: Use image node_id 1 to create a new PyODAnnotator object from inherited base classes: BaseSeriesAnnotator with example: obj = PyODAnnotator(estimator, fmt, labels)