code: def create_model_params(x, y):
      """Create the model and parameters."""
      exp_mod = ExponentialModel(prefix="exp_")
      params = exp_mod.guess(y, x=x)
  
      gauss1 = GaussianModel(prefix="g1_")
      params.update(gauss1.make_params())
  
      gauss2 = GaussianModel(prefix="g2_")
      params.update(gauss2.make_params())
  
      params["g1_center"].set(value=105, min=75, max=125)
      params["g1_sigma"].set(value=15, min=3)
      params["g1_amplitude"].set(value=2000, min=10)
  
      params["g2_center"].set(value=155, min=125, max=175)
      params["g2_sigma"].set(value=15, min=3)
      params["g2_amplitude"].set(value=2000, min=10)
  
      model = gauss1 + gauss2 + exp_mod
      return model, params
  
imports: import json
  import os
  import time
  import numpy
  from numpy.testing import assert_allclose
  import pytest
  from lmfit import Parameters
  import lmfit.jsonutils
  from lmfit.lineshapes import gaussian, lorentzian
  from lmfit.model import Model, ModelResult, load_model, load_modelresult, save_model, save_modelresult
  from lmfit.models import ExponentialModel, ExpressionModel, GaussianModel, VoigtModel
label_desc: Use image node_id 3 for calling a global function with example usage: create_model_params(x, y) and returns: model,  params