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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 initialize(self, endog, freq_weights):
        '''
        Initialize the response variable.

        Parameters
        ----------
        endog : array
            Endogenous response variable

        Returns
        --------
        If `endog` is binary, returns `endog`

        If `endog` is a 2d array, then the input is assumed to be in the format
        (successes, failures) and
        successes/(success + failures) is returned.  And n is set to
        successes + failures.
        '''
        # if not np.all(np.asarray(freq_weights) == 1):
        #     self.variance = V.Binomial(n=freq_weights)
        if (endog.ndim > 1 and endog.shape[1] > 1):
            y = endog[:, 0]
            # overwrite self.freq_weights for deviance below
            self.n = endog.sum(1)
            return y*1./self.n, self.n
        else:
            return endog, np.ones(endog.shape[0])