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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_acf(x, axis=0, fast=False):
 """ Estimate the autocorrelation function of a time series using the FFT. :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 fast: (optional) If ``True``, only use the largest ``2^n`` entries for efficiency. (default: False) """

    x = np.atleast_1d(x)
    m = [slice(None), ] * len(x.shape)

    # For computational efficiency, crop the chain to the largest power of
    # two if requested.
    if fast:
        n = int(2 ** np.floor(np.log2(x.shape[axis])))
        m[axis] = slice(0, n)
        x = x
    else:
        n = x.shape[axis]

    # Compute the FFT and then (from that) the auto-correlation function.
    f = np.fft.fft(x - np.mean(x, axis=axis), n=2 * n, axis=axis)
    m[axis] = slice(0, n)
    acf = np.fft.ifft(f * np.conjugate(f), axis=axis)[tuple(m)].real
    m[axis] = 0
    return acf / acf[tuple(m)]