code: def test_calc_approx_inv_cov():
      np.random.seed(435265)
      X = np.random.normal(size=(50, 3))
      ghat_l = []
      that_l = []
      for i in range(3):
          ghat = _calc_nodewise_row(X, i, 0.01)
          that = _calc_nodewise_weight(X, ghat, i, 0.01)
          ghat_l.append(ghat)
          that_l.append(that)
      theta_hat = _calc_approx_inv_cov(
          np.array(ghat_l), np.array(that_l)
      )
      assert_equal(theta_hat.shape, (3, 3))
  
imports: import numpy
  from numpy.testing import assert_equal, assert_
  from statsmodels.stats.regularized_covariance import _calc_nodewise_row, _calc_nodewise_weight, _calc_approx_inv_cov, RegularizedInvCovariance
label_desc: Use image node_id 3 for calling a global function with example usage: test_calc_approx_inv_cov() without return types