code: def test_unrelativize(self) -> None:
      means_t = np.array([-100.0, 101.0, 200.0, 300.0, 400.0])
      sems_t = np.array([2.0, 3.0, 2.0, 4.0, 0.0])
      mean_c = 200.0
      sem_c = 2.0
  
      for (
          bias_correction,
          cov_means,
          as_percent,
          control_as_constant,
      ) in product(
          (True, False, None),
          (0.5, 0.0),
          (True, False, None),
          (True, False, None),
      ):
          rel_mean_t, rel_sems_t = relativize(
              means_t,
              sems_t,
              mean_c,
              sem_c,
              cov_means=cov_means,
              bias_correction=bias_correction,
              as_percent=as_percent,
              control_as_constant=control_as_constant,
          )
          unrel_mean_t, unrel_sems_t = unrelativize(
              rel_mean_t,
              rel_sems_t,
              mean_c,
              sem_c,
              cov_means=cov_means,
              bias_correction=bias_correction,
              as_percent=as_percent,
              control_as_constant=control_as_constant,
          )
          self.assertTrue(np.allclose(means_t, unrel_mean_t))
          self.assertTrue(np.allclose(sems_t, unrel_sems_t))
  
imports: from itertools import product
  import numpy
  import pandas
  from ax.core.data import Data
  from ax.utils.common.testutils import TestCase
  from ax.utils.stats.statstools import inverse_variance_weight, marginal_effects, relativize, relativize_data, unrelativize
label_desc: Use image node_id 1 for calling the UnrelativizeTest obj's underlying member method code with example usage: obj.test_unrelativize() without return types