class_docstr: Base class for all HMM wrappers, handles required overlap between packages.
docstr: Convert input X into the format needed.
  
  Parameters
  ----------
  X : arraylike (1D np.ndarray or pd.series), shape = [num_observations]
      Observations to apply labels to.
  
  Returns
  -------
  X : arraylike (2D np.ndarray), shape = [1, num_observations]
      Observations to apply labels to.
  series: bool - whether or not X was originally a pd.Series
  index: pd.index, the index if X was originally a series object.
code: def _fix_input(X):
      """Convert input X into the format needed.
  
      Parameters
      ----------
      X : arraylike (1D np.ndarray or pd.series), shape = [num_observations]
          Observations to apply labels to.
  
      Returns
      -------
      X : arraylike (2D np.ndarray), shape = [1, num_observations]
          Observations to apply labels to.
      series: bool - whether or not X was originally a pd.Series
      index: pd.index, the index if X was originally a series object.
      """
      series = isinstance(X, pd.Series)
      index = None
      if series:
          index = X.index
          X = (X.to_numpy()).reshape((-1, 1))
      if isinstance(X, np.ndarray) and X.ndim == 1:
          X = X.reshape((-1, 1))
      return X, series, index
  
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 2 for calling the BaseHMMLearn obj's underlying member method code with example usage: obj._fix_input(X) and returns: X,  series,  index