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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 fit(self, ini_betas=None, tol=1.0e-6, max_iter=200, solve='iwls'):
 """ Method that fits a model with a particular estimation routine. Parameters ini_betas : array k*1, initial coefficient values, including constant. Default is None, which calculates initial values during estimation. tol: float Tolerence for estimation convergence. max_iter : integer Maximum number of iterations if convergence not achieved. solve :string Technique to solve MLE equations. 'iwls' = iteratively (re)weighted least squares (default) """

        self.fit_params['ini_betas'] = ini_betas
        self.fit_params['tol'] = tol
        self.fit_params['max_iter'] = max_iter
        self.fit_params['solve'] = solve
        if solve.lower() == 'iwls':
            params, predy, w, n_iter = iwls(
                self.y, self.X, self.family, self.offset, self.y_fix, ini_betas, tol, max_iter)
            self.fit_params['n_iter'] = n_iter
        return GLMResults(self, params.flatten(), predy, w)