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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def trajectories(self, M, N, start=None, stop=None):
 """ Generates M trajectories, each of length N, starting from state s Parameters M : int number of trajectories 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 """

        trajs = [self.trajectory(N, start=start, stop=stop) for _ in range(M)]
        return trajs