code: def get_linearmodel(slope=0.8, intercept=0.5, noise=1.5):
      # create data to be fitted
      np.random.seed(88)
      x = np.linspace(0, 10, 101)
      y = intercept + x * slope
      y = y + np.random.normal(size=len(x), scale=noise)
  
      model = LinearModel()
      params = model.make_params(intercept=intercept, slope=slope)
  
      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 1 for calling a global function with example usage: get_linearmodel(slope, intercept, noise) and returns: x,  y,  model,  params