code: def _calc_nodewise_weight(exog, nodewise_row, idx, alpha):
      """calculates the nodewise_weightvalue for the idxth variable, used to
      estimate approx_inv_cov.
  
      Parameters
      ----------
      exog : array_like
          The weighted design matrix for the current partition.
      nodewise_row : array_like
          The nodewise_row values for the current variable.
      idx : scalar
          Index of the current variable
      alpha : scalar or array_like
          The penalty weight.  If a scalar, the same penalty weight
          applies to all variables in the model.  If a vector, it
          must have the same length as `params`, and contains a
          penalty weight for each coefficient.
  
      Returns
      -------
      A scalar
  
      Notes
      -----
  
      nodewise_weight_i = sqrt(1/n ||exog,i - exog_-i nodewise_row||_2^2
                               + alpha ||nodewise_row||_1)
      """
  
      n, p = exog.shape
      ind = list(range(p))
      ind.pop(idx)
  
      # handle array alphas
      if not np.isscalar(alpha):
          alpha = alpha[ind]
  
      d = (
          np.linalg.norm(exog[:, idx] - exog[:, ind].dot(nodewise_row))
          ** 2
      )
      d = np.sqrt(d / n + alpha * np.linalg.norm(nodewise_row, 1))
      return d
  
file_path: statsmodels/statsmodels/stats/regularized_covariance.py
imports: from statsmodels.regression.linear_model import OLS
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: _calc_nodewise_weight(exog, nodewise_row, idx, alpha) and returns: d