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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 deviance(self, endog, mu, freq_weights=1, scale=1., axis=None):
        r'''
        Deviance function for either Bernoulli or Binomial data.

        Parameters
        ----------
        endog : array-like
            Endogenous response variable (already transformed to a probability
            if appropriate).
        mu : array
            Fitted mean response variable
        freq_weights : array-like
            1d array of frequency weights. The default is 1.
        scale : float, optional
            An optional scale argument. The default is 1.

        Returns
        --------
        deviance : float
            The deviance function as defined below

        '''
        if np.shape(self.n) == () and self.n == 1:
            one = np.equal(endog, 1)
            return -2 * np.sum((one * np.log(mu + 1e-200) + (1-one) *
                               np.log(1 - mu + 1e-200)) * freq_weights, axis=axis)

        else:
            return 2 * np.sum(self.n * freq_weights *
                              (endog * np.log(endog/mu + 1e-200) +
                               (1 - endog) * np.log((1 - endog) /
                               (1 - mu) + 1e-200)), axis=axis)