code: def init_gaussian_hmm(observations, nstates, lag=1, reversible=True):
      
      from bhmm.init import gaussian
      if lag > 1:
          observations = lag_observations(observations, lag)
      hmm0 = gaussian.init_model_gaussian1d(observations, nstates, reversible=reversible)
      hmm0._lag = lag
      return hmm0
docs: Use a heuristic scheme to generate an initial model.
  
      Parameters
      ----------
      observations : list of ndarray((T_i))
          list of arrays of length T_i with observation data
      nstates : int
          The number of states.
  
      Examples
      --------
  
      Generate initial model for a gaussian output model.
  
      >>> import bhmm
      >>> [model, observations, states] = bhmm.testsystems.generate_synthetic_observations(output='gaussian')
      >>> initial_model = init_gaussian_hmm(observations, model.nstates)