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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 _brentq_cdf(self, value):
 """Helper function to compute percent_point. As scipy.stats.gaussian_kde doesn't provide this functionality out of the box we need to make a numerical approach: - First we scalarize and bound cumulative_distribution. - Then we define a function `f(x) = cdf(x) - value`, where value is the given argument. - As value will be called from ppf we can assume value = cdf(z) for some z that is the value we are searching for. Therefore the zeros of the function will be x such that: cdf(x) - cdf(z) = 0 => (becasue cdf is monotonous and continous) x = z Args: value(float):
 cdf value, that is, in [0,1] Returns: callable: function whose zero is the ppf of value. """

        # The decorator expects an instance method, but usually are decorated before being bounded
        bound_cdf = partial(scalarize(GaussianKDE.cumulative_distribution), self)

        def f(x):
            return bound_cdf(x) - value

        return f