code: def _calc_grad(mod, params, alpha, L1_wt, score_kwds):
      """calculates the log-likelihood gradient for the debiasing
  
      Parameters
      ----------
      mod : statsmodels model class instance
          The model for the current partition.
      params : array_like
          The estimated coefficients for the current partition.
      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.
      L1_wt : scalar
          The fraction of the penalty given to the L1 penalty term.
          Must be between 0 and 1 (inclusive).  If 0, the fit is
          a ridge fit, if 1 it is a lasso fit.
      score_kwds : dict-like or None
          Keyword arguments for the score function.
  
      Returns
      -------
      An array-like object of the same dimension as params
  
      Notes
      -----
      In general:
  
      gradient l_k(params)
  
      where k corresponds to the index of the partition
  
      For OLS:
  
      X^T(y - X^T params)
      """
  
      grad = -mod.score(np.asarray(params), **score_kwds)
      grad += alpha * (1 - L1_wt)
      return grad
  
imports: from statsmodels.base.elastic_net import RegularizedResults
  from statsmodels.stats.regularized_covariance import _calc_nodewise_row, _calc_nodewise_weight, _calc_approx_inv_cov
  from statsmodels.base.model import LikelihoodModelResults
  from statsmodels.regression.linear_model import OLS
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: _calc_grad(mod, params, alpha, L1_wt, score_kwds) and returns: grad