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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 _check_branching(X,Xsamples,restart,threshold=0.25):
 """\ Check whether time series branches. Parameters X (np.array):
 current time series data. Xsamples (np.array):
 list of previous branching samples. restart (int):
 counts number of restart trials. threshold (float, optional):
 sets threshold for attractor identification. Returns ------- check : bool true if branching realization Xsamples updated list """

    check = True
    if restart == 0:
        Xsamples.append(X)
    else:
        for Xcompare in Xsamples:
            Xtmax_diff = np.absolute(X[-1,:] - Xcompare[-1,:])
            # If the second largest element is smaller than threshold
            # set check to False, i.e. at least two elements
            # need to change in order to have a branching.
            # If we observe all parameters of the system,
            # a new attractor state must involve changes in two
            # variables.
            if np.partition(Xtmax_diff,-2)[-2] < threshold:
                check = False
        if check:
            Xsamples.append(X)
    if not check:
        logg.m('realization {}:'.format(restart), 'no new branch', v=4)
    else:
        logg.m('realization {}:'.format(restart), 'new branch', v=4)
    return check, Xsamples