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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 bootstrap_counts(dtrajs, lagtime, corrlength=None):
 r"""Generates a randomly resampled count matrix given the input coordinates. Parameters dtrajs : array-like or array-like of array-like single or multiple discrete trajectories. Every trajectory is assumed to be a statistically independent realization. Note that this is often not true and is a weakness with the present bootstrapping approach. lagtime : int the lag time at which the count matrix will be evaluated corrlength : int, optional, default=None the correlation length of the discrete trajectory. N / corrlength counts will be generated, where N is the total number of frames. If set to None (default), corrlength = lagtime will be used. Notes ----- This function can be called multiple times in order to generate randomly resampled realizations of count matrices. For each of these realizations you can estimate a transition matrix, and from each of them computing the observables of your interest. The standard deviation of such a sample of the observable is a model for the standard error. The bootstrap will be generated by sampling N/corrlength counts at time tuples (t, t+lagtime), where t is uniformly sampled over all trajectory time frames in [0,n_i-lagtime]. Here, n_i is the length of trajectory i and N = sum_i n_i is the total number of frames. See also -------- bootstrap_trajectories """

    dtrajs = _ensure_dtraj_list(dtrajs)
    return dense.bootstrapping.bootstrap_counts(dtrajs, lagtime, corrlength=corrlength)