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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 implied_timescales(sequences, lag_times, n_timescales=10, msm=None, n_jobs=1, verbose=0):
 """ Calculate the implied timescales for a given MSM. Parameters sequences : list of array-like List of sequences, or a single sequence. Each sequence should be a 1D iterable of state labels. Labels can be integers, strings, or other orderable objects. lag_times : array-like Lag times to calculate implied timescales at. n_timescales : int, optional Number of timescales to calculate. msm : msmbuilder.msm.MarkovStateModel, optional Instance of an MSM to specify parameters other than the lag time. If None, then the default parameters (as implemented by msmbuilder.msm.MarkovStateModel) will be used. n_jobs : int, optional Number of jobs to run in parallel Returns ------- timescales : np.ndarray, shape = [n_models, n_timescales] The slowest timescales (in units of lag times) for each model. """


    if msm is None:
        msm = MarkovStateModel()

    param_grid = {'lag_time' : lag_times}
    models = param_sweep(msm, sequences, param_grid, n_jobs=n_jobs,
                         verbose=verbose)
    timescales = [m.timescales_ for m in models]
    n_timescales = min(n_timescales, min(len(ts) for ts in timescales))
    timescales = np.array([ts[:n_timescales] for ts in timescales])
    return timescales