code: def test_scalar_minimize_neg_value():
      x0 = 3.14
      fmin = -1.1
      xtol = 0.001
      ftol = 2.0 * xtol
  
      def objective(pars):
          return (pars["x"] - x0) ** 2.0 + fmin
  
      params = Parameters()
      params.add("x", value=2 * x0)
  
      minr = Minimizer(objective, params)
      result = minr.scalar_minimize(
          method="Nelder-Mead", options={"xatol": xtol, "fatol": ftol}
      )
      assert abs(result.params["x"].value - x0) < xtol
      assert abs(result.fun - fmin) < ftol
  
imports: import numpy
  import pytest
  from lmfit import Minimizer, Parameters
  from lmfit.models import GaussianModel
label_desc: Use image node_id 1 for calling a global function with example usage: test_scalar_minimize_neg_value() and returns: unknown