class_docstr: Base class for all HMM wrappers, handles required overlap between packages.
docstr: Ensure X is correct type, then fit wrapped estimator.
  
  Parameters
  ----------
  X : arraylike (1D np.ndarray or pd.series), shape = [num_observations]
      Observations to apply labels to.
  
  Returns
  -------
  self :
      Reference to self.
code: def _fit(self, X, Y=None):
      """Ensure X is correct type, then fit wrapped estimator.
  
      Parameters
      ----------
      X : arraylike (1D np.ndarray or pd.series), shape = [num_observations]
          Observations to apply labels to.
  
      Returns
      -------
      self :
          Reference to self.
      """
      X, _, _ = self._fix_input(X)
      self._hmm_estimator = self._hmm_estimator.fit(X)
      return self
  
file_path: sktime/sktime/annotation/hmm_learn/base.py
imports: import numpy
  import pandas
  from sktime.annotation.base import BaseSeriesAnnotator
label_desc: Use image node_id 3 for calling the BaseHMMLearn obj's underlying member method code with example usage: obj._fit(X, Y) and returns: self