code: def test_loop_vectorized_batch_equivalence():
      # test equality of loop, vectorized, batch-vectorized
      nobs = 200
  
      np.random.seed(8765679)
      resu1 = bootstrap(
          NewNorm(),
          args=(0, 1),
          nobs=nobs,
          nrep=100,
          value=0.576 / (1 + 4.0 / nobs - 25.0 / nobs**2),
      )
  
      np.random.seed(8765679)
      tmp = [
          bootstrap(NewNorm(), args=(0, 1), nobs=nobs, nrep=1)
          for _ in range(100)
      ]
      resu2 = (
          np.array(tmp) > 0.576 / (1 + 4.0 / nobs - 25.0 / nobs**2)
      ).mean()
  
      np.random.seed(8765679)
      tmp = [
          bootstrap(
              NewNorm(),
              args=(0, 1),
              nobs=nobs,
              nrep=1,
              value=0.576 / (1 + 4.0 / nobs - 25.0 / nobs**2),
              batch_size=10,
          )
          for _ in range(10)
      ]
      resu3 = np.array(tmp).mean()
  
      assert_array_almost_equal(resu1, resu2, 15)
      assert_array_almost_equal(resu2, resu3, 15)
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal
  from statsmodels.sandbox.distributions.gof_new import bootstrap, NewNorm
label_desc: Use image node_id 1 for calling a global function with example usage: test_loop_vectorized_batch_equivalence() without return types