<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def lag_observations(observations, lag, stride=1):
 r""" Create new trajectories that are subsampled at lag but shifted at lag times larger than 1 without discarding data. Do not use this function for Bayesian estimators, where data must be given such that subsequent transitions are uncorrelated. Parameters observations : list of int arrays observation trajectories lag : int lag time stride : int, default=1 will return only one trajectory for every stride. Use this for Bayesian analysis. """

    obsnew = []
    for obs in observations:
        for shift in range(0, lag, stride):
            obs_lagged = (obs[shift:][::lag])
            if len(obs_lagged) > 1:
                obsnew.append(obs_lagged)
    return obsnew