<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def generate_traj(P, N, start=None, stop=None, dt=1):
 """ Generates a realization of the Markov chain with transition matrix P. Parameters P : (n, n) ndarray transition matrix 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 dt : int trajectory will be saved every dt time steps. Internally, the dt'th power of P is taken to ensure a more efficient simulation. Returns ------- traj_sliced : (N/dt, ) ndarray A discrete trajectory with length N/dt """

    sampler = MarkovChainSampler(P, dt=dt)
    return sampler.trajectory(N, start=start, stop=stop)