code: def margeff_cov_params(
      model,
      params,
      exog,
      cov_params,
      at,
      derivative,
      dummy_ind,
      count_ind,
      method,
      J,
  ):
      """
      Computes the variance-covariance of marginal effects by the delta method.
  
      Parameters
      ----------
      model : model instance
          The model that returned the fitted results. Its pdf method is used
          for computing the Jacobian of discrete variables in dummy_ind and
          count_ind
      params : array_like
          estimated model parameters
      exog : array_like
          exogenous variables at which to calculate the derivative
      cov_params : array_like
          The variance-covariance of the parameters
      at : str
         Options are:
  
          - 'overall', The average of the marginal effects at each
            observation.
          - 'mean', The marginal effects at the mean of each regressor.
          - 'median', The marginal effects at the median of each regressor.
          - 'zero', The marginal effects at zero for each regressor.
          - 'all', The marginal effects at each observation.
  
          Only overall has any effect here.you
  
      derivative : function or array_like
          If a function, it returns the marginal effects of the model with
          respect to the exogenous variables evaluated at exog. Expected to be
          called derivative(params, exog). This will be numerically
          differentiated. Otherwise, it can be the Jacobian of the marginal
          effects with respect to the parameters.
      dummy_ind : array_like
          Indices of the columns of exog that contain dummy variables
      count_ind : array_like
          Indices of the columns of exog that contain count variables
  
      Notes
      -----
      For continuous regressors, the variance-covariance is given by
  
      Asy. Var[MargEff] = [d margeff / d params] V [d margeff / d params]'
  
      where V is the parameter variance-covariance.
  
      The outer Jacobians are computed via numerical differentiation if
      derivative is a function.
      """
      if callable(derivative):
          from statsmodels.tools.numdiff import approx_fprime_cs
  
          params = params.ravel("F")  # for Multinomial
          try:
              jacobian_mat = approx_fprime_cs(
                  params, derivative, args=(exog, method)
              )
          except TypeError:  # norm.cdf does not take complex values
              from statsmodels.tools.numdiff import approx_fprime
  
              jacobian_mat = approx_fprime(
                  params, derivative, args=(exog, method)
              )
          if at == "overall":
              jacobian_mat = np.mean(jacobian_mat, axis=1)
          else:
              jacobian_mat = (
                  jacobian_mat.squeeze()
              )  # exog was 2d row vector
          if dummy_ind is not None:
              jacobian_mat = _margeff_cov_params_dummy(
                  model,
                  jacobian_mat,
                  params,
                  exog,
                  dummy_ind,
                  method,
                  J,
              )
          if count_ind is not None:
              jacobian_mat = _margeff_cov_params_count(
                  model,
                  jacobian_mat,
                  params,
                  exog,
                  count_ind,
                  method,
                  J,
              )
      else:
          jacobian_mat = derivative
  
      # NOTE: this will not go through for at == 'all'
      return np.dot(np.dot(jacobian_mat, cov_params), jacobian_mat.T)
  
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 14 for calling a global function with example usage: margeff_cov_params(model, params, exog, cov_params, at, derivative, dummy_ind, count_ind, method, J) and returns: np