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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 systematic_resample(weights):
 """ Performs the systemic resampling algorithm used by particle filters. This algorithm separates the sample space into N divisions. A single random offset is used to to choose where to sample from for all divisions. This guarantees that every sample is exactly 1/N apart. Parameters weights : list-like of float list of weights as floats Returns ------- indexes : ndarray of ints array of indexes into the weights defining the resample. i.e. the index of the zeroth resample is indexes[0], etc. """

    N = len(weights)

    # make N subdivisions, and choose positions with a consistent random offset
    positions = (random() + np.arange(N)) / N

    indexes = np.zeros(N, 'i')
    cumulative_sum = np.cumsum(weights)
    i, j = 0, 0
    while i < N:
        if positions[i] < cumulative_sum[j]:
            indexes[i] = j
            i += 1
        else:
            j += 1
    return indexes