code: def generate_synthetic_state_trajectory(self, nsteps, initial_Pi=None, start=None, stop=None, dtype=np.int32):
          
          # consistency check
          if initial_Pi is not None and start is not None:
              raise ValueError('Arguments initial_Pi and start are exclusive. Only set one of them.')
  
          # Generate first state sample.
          if start is None:
              if initial_Pi is not None:
                  start = np.random.choice(range(self._nstates), size=1, p=initial_Pi)
              else:
                  start = np.random.choice(range(self._nstates), size=1, p=self._Pi)
  
          # Generate and return trajectory
          from msmtools import generation as msmgen
          traj = msmgen.generate_traj(self.transition_matrix, nsteps, start=start, stop=stop, dt=1)
          return traj.astype(dtype)
docs: Generate a synthetic state trajectory.
  
          Parameters
          ----------
          nsteps : int
              Number of steps in the synthetic state trajectory to be generated.
          initial_Pi : np.array of shape (nstates,), optional, default=None
              The initial probability distribution, if samples are not to be taken from the intrinsic
              initial distribution.
          start : int
              starting state. Exclusive with initial_Pi
          stop : int
              stopping state. Trajectory will terminate when reaching the stopping state before length number of steps.
          dtype : numpy.dtype, optional, default=numpy.int32
              The numpy dtype to use to store the synthetic trajectory.
  
          Returns
          -------
          states : np.array of shape (nstates,) of dtype=np.int32
              The trajectory of hidden states, with each element in range(0,nstates).
  
          Examples
          --------
  
          Generate a synthetic state trajectory of a specified length.
  
          >>> from bhmm import testsystems
          >>> model = testsystems.dalton_model()
          >>> states = model.generate_synthetic_state_trajectory(nsteps=100)