code: def get_gaussianmodel(
      amplitude=1.0, center=5.0, sigma=1.0, noise=0.1
  ):
      # create data to be fitted
      np.random.seed(7392)
      x = np.linspace(-20, 20, 201)
      y = gaussian(x, amplitude, center=center, sigma=sigma)
      y = y + np.random.normal(size=len(x), scale=noise)
  
      model = GaussianModel()
      params = model.make_params(
          amplitude=amplitude / 5.0,
          center=center - 1.0,
          sigma=sigma * 2.0,
      )
      return x, y, model, params
  
file_path: lmfit-py/tests/test_model_uncertainties.py
imports: import os
  import numpy
  from numpy.testing import assert_allclose
  from lmfit.lineshapes import gaussian
  from lmfit.models import ExponentialModel, GaussianModel, LinearModel
label_desc: Use image node_id 2 for calling a global function with example usage: get_gaussianmodel(amplitude, center, sigma, noise) and returns: x,  y,  model,  params