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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 statistical_inefficiencies(dtrajs, lag, C=None, truncate_acf=True, mact=2.0, n_jobs=1, callback=None):
 r""" Computes statistical inefficiencies of sliding-window transition counts at given lag we collect the target sequence .. mathh: Y^(i) = {x_{t+\tau} | x_{t}=i} which contains the time-ordered target states at times :math:`t+\tau` whenever we started in state :math:`i` at time :math:`t`. Then we define the indicator sequence: .. math: a^{(i,j)}_t (\tau) = 1(Y^(i)_t = j) The statistical inefficiency for transition counts :math:`c_{ij}(tau)` is computed as the statistical inefficiency of the sequence :math:`a^{(i,j)}_t (\tau)`. Parameters dtrajs : list of int-iterables discrete trajectories lag : int lag time C : scipy sparse matrix (n, n) or None sliding window count matrix, if already available truncate_acf : bool, optional, default=True When the normalized autocorrelation function passes through 0, it is truncated in order to avoid integrating random noise n_jobs: int, default=1 If greater one, the function will be evaluated with multiple processes. callback: callable, default=None will be called for every statistical inefficiency computed (number of nonzero elements in count matrix). If n_jobs is greater one, the callback will be invoked per finished batch. Returns ------- I : scipy sparse matrix (n, n) Statistical inefficiency matrix with a sparsity pattern identical to the sliding-window count matrix at the same lag time. Will contain a statistical inefficiency :math:`I_{ij} \in (0,1]` whenever there is a count :math:`c_{ij} > 0`. When there is no transition count (:math:`c_{ij} = 0`), the statistical inefficiency is 0. See also -------- msmtools.util.statistics.statistical_inefficiency used to compute the statistical inefficiency for conditional trajectories """

    # count matrix
    if C is None:
        C = count_matrix_coo2_mult(dtrajs, lag, sliding=True, sparse=True)
    if callback is not None:
        if not callable(callback):
            raise ValueError('Provided callback is not callable')
    # split sequences
    splitseq = _split_sequences_multitraj(dtrajs, lag)
    # compute inefficiencies
    I, J = C.nonzero()
    if n_jobs > 1:
        from multiprocessing.pool import Pool, MapResult
        from contextlib import closing
        import tempfile

        # to avoid pickling partial results, we store these in a numpy.memmap
        ntf = tempfile.NamedTemporaryFile(delete=False)
        arr = np.memmap(ntf.name, dtype=np.float64, mode='w+', shape=C.nnz)
        #arr[:] = np.nan
        gen = _arguments_generator(I, J, splitseq, truncate_acf=truncate_acf, mact=truncate_acf,
                                   array=ntf.name, njobs=n_jobs)
        if callback:
            x = gen.n_blocks()
            _callback = lambda _: callback(x)
        else:
            _callback = callback
        with closing(Pool(n_jobs)) as pool:
            result_async = [pool.apply_async(_wrapper, (args,), callback=_callback)
                            for args in gen]

            [t.get() for t in result_async]
            data = np.array(arr[:])
            #assert np.all(np.isfinite(data))
        import os
        os.unlink(ntf.name)
    else:
        data = np.empty(C.nnz)
        for index, (i, j) in enumerate(zip(I, J)):
            data[index] = statistical_inefficiency(_indicator_multitraj(splitseq, i, j),
                                                   truncate_acf=truncate_acf, mact=mact)
            if callback is not None:
                callback(1)
    res = csr_matrix((data, (I, J)), shape=C.shape)
    return res