<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def trajectory(self, N, start=None, stop=None):
 """ Generates a trajectory realization of length N, starting from state s Parameters N : int trajectory length start : int, optional, default = None starting state. If not given, will sample from the stationary distribution of P stop : int or int-array-like, optional, default = None stopping set. If given, the trajectory will be stopped before N steps once a state of the stop set is reached """

        # check input
        stop = types.ensure_int_vector_or_None(stop, require_order=False)

        if start is None:
            if self.mudist is None:
                # compute mu, the stationary distribution of P
                import msmtools.analysis as msmana
                from scipy.stats import rv_discrete

                mu = msmana.stationary_distribution(self.P)
                self.mudist = rv_discrete(values=(np.arange(self.n), mu))
            # sample starting point from mu
            start = self.mudist.rvs()

        # evaluate stopping set
        stopat = np.ndarray((self.n), dtype=bool)
        stopat[:] = False
        if (stop is not None):
            for s in np.array(stop):
                stopat[s] = True

        # result
        traj = np.zeros(N, dtype=int)
        traj[0] = start
        # already at stopping state?
        if stopat[traj[0]]:
            return traj[:1]
        # else run until end or stopping state
        for t in range(1, N):
            traj[t] = self.rgs[traj[t - 1]].rvs()
            if stopat[traj[t]]:
                return traj[:t+1]
        # return
        return traj