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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 autocov(x, **kwargs):
 """Returns the autocovariance of signal s at all lags. Parameters x : ndarray axis : time axis all_lags : {True/False} whether to return all nonzero lags, or to clip the length of r_xy to be the length of x and y. If False, then the zero lag correlation is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2 Returns ------- cxx : ndarray The autocovariance function Notes ----- Adheres to the definition .. math:: C_{xx}[k]=E\{(X[n+k]-E\{X\})(X[n]-E\{X\})^{*}\} where X is a discrete, stationary (ergodic) random process """

    # only remove the mean once, if needed
    debias = kwargs.pop('debias', True)
    axis = kwargs.get('axis', -1)
    if debias:
        x = remove_bias(x, axis)
    kwargs['debias'] = False
    return crosscov(x, x, **kwargs)