code: def _calc_nodewise_row(exog, idx, alpha):
      """calculates the nodewise_row values for the idxth variable, used to
      estimate approx_inv_cov.
  
      Parameters
      ----------
      exog : array_like
          The weighted design matrix for the current partition.
      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
      -------
      An array-like object of length p-1
  
      Notes
      -----
  
      nodewise_row_i = arg min 1/(2n) ||exog_i - exog_-i gamma||_2^2
                               + alpha ||gamma||_1
      """
  
      p = exog.shape[1]
      ind = list(range(p))
      ind.pop(idx)
  
      # handle array alphas
      if not np.isscalar(alpha):
          alpha = alpha[ind]
  
      tmod = OLS(exog[:, idx], exog[:, ind])
  
      nodewise_row = tmod.fit_regularized(alpha=alpha).params
  
      return nodewise_row
  
file_path: statsmodels/statsmodels/stats/regularized_covariance.py
imports: from statsmodels.regression.linear_model import OLS
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: _calc_nodewise_row(exog, idx, alpha) and returns: nodewise_row