human
Solve the following python programming problem given the following code:
@ classmethod 
def setup_class(self) : 
    self.params_tol = [5e-05, 5e-06] 
    self.bse_tol = [5e-06, 0.1] 
    exog = exog_st 
    start = OLS (endog, exog).fit ().params 
    (nobs, k_instr) = instrument.shape 
    w0inv = (np.dot (instrument.T, instrument) / nobs) 
    def func(params, exog) : 
        return np.dot (exog, params) 
mod = gmm.NonlinearIVGMM (endog, exog, instrument, func) 
    res = mod.fit (k_instr, maxiter = 1, inv_weights = w0inv, optim_method = 'bfgs', optim_args = { 
        'gtol' : 1e-08, 
        'disp' : 0, 
}, wargs = { 
        'centered' : False, 
}, has_optimal_weights = False) 
    self.res1 = res 
    mod = gmm.IVGMM (endog, exog, instrument) 
    res = mod.fit (start, maxiter = 1, inv_weights = w0inv, optim_method = 'bfgs', optim_args = { 
        'gtol' : 1e-06, 
        'disp' : 0, 
}, wargs = { 
        'centered' : False, 
}, has_optimal_weights = False) 
    self.res3 = res 
    from.results_gmm_griliches import results_onestep as results 
    self.res2 = results 

    
    

    Fix the buggy line: res = mod.fit (k_instr, maxiter = 1, inv_weights = w0inv, optim_method = 'bfgs', optim_args = {
assistant
#Here is Python code to fix the buggy line: res = mod.

res = mod.fit (start, maxiter = 1, inv_weights = w0inv, optim_method = 'bfgs', optim_args = {