code: def func_lmfit(p):
      par = p.valuesdict()
      f1 = (
          par["a"] * par["x"] ** 2
          + par["b"] * par["x"] * par["y"]
          + par["c"] * par["y"] ** 2
          + par["d"] * par["x"]
          + par["e"] * par["y"]
          + par["f"]
      )
      f2 = (
          -1.0
          * par["g"]
          * np.exp(
              -((par["x"] - par["h"]) ** 2 + (par["y"] - par["i"]) ** 2)
              / par["scale"]
          )
      )
      f3 = (
          -1.0
          * par["j"]
          * np.exp(
              -((par["x"] - par["k"]) ** 2 + (par["y"] - par["l"]) ** 2)
              / par["scale"]
          )
      )
  
      return f1 + f2 + f3
  
imports: import pickle
  import numpy
  from numpy.testing import assert_allclose, assert_equal
  import pytest
  from scipy import optimize
  import lmfit
label_desc: Use image node_id 2 for calling a global function with example usage: func_lmfit(p) and returns: unknown