code: def margeff_cov_with_se(
      model,
      params,
      exog,
      cov_params,
      at,
      derivative,
      dummy_ind,
      count_ind,
      method,
      J,
  ):
      """
      See margeff_cov_params.
  
      Same function but returns both the covariance of the marginal effects
      and their standard errors.
      """
      cov_me = margeff_cov_params(
          model,
          params,
          exog,
          cov_params,
          at,
          derivative,
          dummy_ind,
          count_ind,
          method,
          J,
      )
      return cov_me, np.sqrt(np.diag(cov_me))
  
imports: from statsmodels.compat.python import lzip
  import numpy
  from scipy.stats import norm
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 15 for calling a global function with example usage: margeff_cov_with_se(model, params, exog, cov_params, at, derivative, dummy_ind, count_ind, method, J) and returns: cov_me,  np