code: def bootstrap_counts(dtrajs, lagtime, corrlength=None):
      r
      dtrajs = _ensure_dtraj_list(dtrajs)
      return dense.bootstrapping.bootstrap_counts(dtrajs, lagtime, corrlength=corrlength)
docs: 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