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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 get_integrated_act(x, axis=0, window=50, fast=False):
 """ Estimate the integrated autocorrelation time of a time series. See `Sokal's notes <http://www.stat.unc.edu/faculty/cji/Sokal.pdf>`_ on MCMC and sample estimators for autocorrelation times. :param x: The time series. If multidimensional, set the time axis using the ``axis`` keyword argument and the function will be computed for every other axis. :param axis: (optional) The time axis of ``x``. Assumed to be the first axis if not specified. :param window: (optional) The size of the window to use. (default: 50) :param fast: (optional) If ``True``, only use the largest ``2^n`` entries for efficiency. (default: False) """

    # Compute the autocorrelation function.
    f = get_acf(x, axis=axis, fast=fast)

    # Special case 1D for simplicity.
    if len(f.shape) == 1:
        return 1 + 2 * np.sum(f[1:window])

    # N-dimensional case.
    m = [slice(None), ] * len(f.shape)
    m[axis] = slice(1, window)
    tau = 1 + 2 * np.sum(f[tuple(m)], axis=axis)

    return tau