code: def test_score_numdiff(noise):
      y, x_mean, x_sc, x_sm, x_no, time, groups = setup1(
          1000, model1, noise
      )
  
      preg = ProcessMLE(y, x_mean, x_sc, x_sm, x_no, time, groups)
  
      def loglike(x):
          return preg.loglike(x)
  
      q = x_mean.shape[1] + x_sc.shape[1] + x_sm.shape[1]
      if noise:
          q += x_no.shape[1]
  
      np.random.seed(342)
  
      atol = 2e-3 if PLATFORM_OSX else 1e-2
      for _ in range(5):
          par0 = preg._get_start()
          par = par0 + 0.1 * np.random.normal(size=q)
          score = preg.score(par)
          score_nd = nd.approx_fprime(par, loglike, epsilon=1e-7)
          assert_allclose(score, score_nd, atol=atol, rtol=1e-4)
  
file_path: statsmodels/statsmodels/regression/tests/test_processreg.py
imports: from statsmodels.compat.platform import PLATFORM_OSX
  from statsmodels.regression.process_regression import ProcessMLE, GaussianCovariance
  import numpy
  import pandas
  import pytest
  import collections
  import statsmodels.tools.numdiff
  from numpy.testing import assert_allclose, assert_equal
label_desc: Use image node_id 7 for calling a global function with example usage: test_score_numdiff(noise) and returns: preg