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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 bootstrapping_count_matrix(Ct, nbs=10000):
 """ Perform bootstrapping on trajectories to estimate uncertainties for singular values of count matrices. Parameters Ct : csr-matrix count matrix of the data. nbs : int, optional the number of re-samplings to be drawn from dtrajs Returns ------- smean : ndarray(N,) mean values of singular values sdev : ndarray(N,) standard deviations of singular values """

    # Get the number of states:
    N = Ct.shape[0]
    # Get the number of transition pairs:
    T = Ct.sum()
    # Reshape and normalize the count matrix:
    p = Ct.toarray()
    p = np.reshape(p, (N*N,)).astype(np.float)
    p = p / T
    # Perform the bootstrapping:
    svals = np.zeros((nbs, N))
    for s in range(nbs):
        # Draw sample:
        sel = np.random.multinomial(T, p)
        # Compute the count-matrix:
        sC = np.reshape(sel, (N, N))
        # Compute singular values:
        svals[s, :] = scl.svdvals(sC)
    # Compute mean and uncertainties:
    smean = np.mean(svals, axis=0)
    sdev = np.std(svals, axis=0)

    return smean, sdev