code: def test_full_output_false(reset_randomstate):
      # newton needs f, score, start, fargs, kwargs
      # bfgs needs f, score start, fargs, kwargs
      # nm needs ""
      # cg ""
      # ncg ""
      # powell ""
      for method in fit_funcs:
          func = fit_funcs[method]
          if method == "newton":
              xopt, retvals = func(
                  dummy_func,
                  dummy_score,
                  [1.0],
                  (),
                  {},
                  hess=dummy_hess,
                  full_output=False,
                  disp=0,
              )
  
          else:
              xopt, retvals = func(
                  dummy_func,
                  dummy_score,
                  [1.0],
                  (),
                  {},
                  full_output=False,
                  disp=0,
              )
          assert_(retvals is None)
          if method == "powell" and SP_LT_15:
              # Fixed in SP 1.5
              assert_(xopt.shape == () and xopt.size == 1)
          else:
              assert_(len(xopt) == 1)
  
file_path: statsmodels/statsmodels/base/tests/test_optimize.py
imports: from statsmodels.compat.scipy import SP_LT_15, SP_LT_17
  import pytest
  from numpy.testing import assert_
  from numpy.testing import assert_almost_equal
  from statsmodels.base.optimizer import _fit_newton, _fit_nm, _fit_bfgs, _fit_cg, _fit_ncg, _fit_powell, _fit_lbfgs, _fit_basinhopping, _fit_minimize
label_desc: Use image node_id 8 for calling a global function with example usage: test_full_output_false(reset_randomstate) without return types