code: def _calc_approx_inv_cov(nodewise_row_l, nodewise_weight_l):
      """calculates the approximate inverse covariance matrix
  
      Parameters
      ----------
      nodewise_row_l : list
          A list of array-like object where each object corresponds to
          the nodewise_row values for the corresponding variable, should
          be length p.
      nodewise_weight_l : list
          A list of scalars where each scalar corresponds to the nodewise_weight
          value for the corresponding variable, should be length p.
  
      Returns
      ------
      An array-like object, p x p matrix
  
      Notes
      -----
  
      nwr = nodewise_row
      nww = nodewise_weight
  
      approx_inv_cov_j = - 1 / nww_j [nwr_j,1,...,1,...nwr_j,p]
      """
  
      p = len(nodewise_weight_l)
  
      approx_inv_cov = -np.eye(p)
      for idx in range(p):
          ind = list(range(p))
          ind.pop(idx)
          approx_inv_cov[idx, ind] = nodewise_row_l[idx]
      approx_inv_cov *= -1 / nodewise_weight_l[:, None] ** 2
  
      return approx_inv_cov
  
file_path: statsmodels/statsmodels/stats/regularized_covariance.py
imports: from statsmodels.regression.linear_model import OLS
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: _calc_approx_inv_cov(nodewise_row_l, nodewise_weight_l) and returns: approx_inv_cov