class_docstr: Base class for all HMM wrappers, handles required overlap between packages.
docstr: Ensure the input type is correct, then predict using wrapped estimator.
  
  Parameters
  ----------
  X : 1D np.array, shape = [num_observations]
      Observations to apply labels to.
  
  Returns
  -------
  annotated_x : array-like, shape = [num_observations]
      Array of predicted class labels, same size as input.
code: def _predict(self, X):
      """Ensure the input type is correct, then predict using wrapped estimator.
  
      Parameters
      ----------
      X : 1D np.array, shape = [num_observations]
          Observations to apply labels to.
  
      Returns
      -------
      annotated_x : array-like, shape = [num_observations]
          Array of predicted class labels, same size as input.
      """
      X, series, index = self._fix_input(X)
      X_prime = self._hmm_estimator.predict(X)
      if series:
          X_prime = pd.Series(X_prime, index=index)
      return X_prime
  
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 4 for calling the BaseHMMLearn obj's underlying member method code with example usage: obj._predict(X) and returns: X_prime