code: def approx_hess3(x, f, epsilon=None, args=(), kwargs={}):
      n = len(x)
      h = _get_epsilon(x, 4, epsilon, n)
      ee = np.diag(h)
      hess = np.outer(h, h)
  
      for i in range(n):
          for j in range(i, n):
              hess[i, j] = np.squeeze(
                  (
                      f(*((x + ee[i, :] + ee[j, :],) + args), **kwargs)
                      - f(
                          *((x + ee[i, :] - ee[j, :],) + args), **kwargs
                      )
                      - (
                          f(
                              *((x - ee[i, :] + ee[j, :],) + args),
                              **kwargs,
                          )
                          - f(
                              *((x - ee[i, :] - ee[j, :],) + args),
                              **kwargs,
                          )
                      )
                  )
                  / (4.0 * hess[i, j])
              )
              hess[j, i] = hess[i, j]
      return hess
  
file_path: statsmodels/statsmodels/tools/numdiff.py
imports: import numpy
  from statsmodels.compat.pandas import Appender, Substitution
label_desc: Use image node_id 9 for calling a global function with example usage: approx_hess3(x, f, epsilon, args, kwargs) and returns: hess