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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 geweke(x, first=.1, last=.5, intervals=20, maxlag=20):
 """Return z-scores for convergence diagnostics. Compare the mean of the first % of series with the mean of the last % of series. x is divided into a number of segments for which this difference is computed. If the series is converged, this score should oscillate between -1 and 1. Parameters x : array-like The trace of some stochastic parameter. first : float The fraction of series at the beginning of the trace. last : float The fraction of series at the end to be compared with the section at the beginning. intervals : int The number of segments. maxlag : int Maximum autocorrelation lag for estimation of spectral variance Returns ------- scores : list [[]] Return a list of [i, score], where i is the starting index for each interval and score the Geweke score on the interval. Notes ----- The Geweke score on some series x is computed by: .. math:: \frac{E[x_s] - E[x_e]}{\sqrt{V[x_s] + V[x_e]}} where :math:`E` stands for the mean, :math:`V` the variance, :math:`x_s` a section at the start of the series and :math:`x_e` a section at the end of the series. References Geweke (1992) """

    
    if not has_sm:
        print("statsmodels not available. Geweke diagnostic cannot be calculated.")
        return

    if np.ndim(x) > 1:
        return [geweke(y, first, last, intervals) for y in np.transpose(x)]

    # Filter out invalid intervals
    if first + last >= 1:
        raise ValueError(
            "Invalid intervals for Geweke convergence analysis",
            (first, last))

    # Initialize list of z-scores
    zscores = [None] * intervals

    # Starting points for calculations
    starts = np.linspace(0, int(len(x)*(1.-last)), intervals).astype(int)

    # Loop over start indices
    for i,s in enumerate(starts):

        # Size of remaining array
        x_trunc = x[s:]
        n = len(x_trunc)

        # Calculate slices
        first_slice = x_trunc[:int(first * n)]
        last_slice = x_trunc[int(last * n):]

        z = (first_slice.mean() - last_slice.mean())
        z /= np.sqrt(spec(first_slice)/len(first_slice) +
                     spec(last_slice)/len(last_slice))
        zscores[i] = len(x) - n, z

    return zscores