docstr: Second derivative of the dependence function
  
  implemented through numerical differentiation
docstr_tok: ["Second","derivative","of","the","dependence","function","implemented","through","numerical","differentiation"]
code: def deriv2(self, t, *args):
      """Second derivative of the dependence function
  
      implemented through numerical differentiation
      """
      if np.size(t) == 1:
          d2 = approx_hess([t], self.evaluate, args=args)[0]
      else:
          d2 = np.array(
              [
                  approx_hess([ti], self.evaluate, args=args)[0, 0]
                  for ti in t
              ]
          )
      return d2
  
imports: import numpy
  from scipy import stats
  from statsmodels.tools.numdiff import _approx_fprime_cs_scalar, approx_hess
label_desc: Use image node_id 4 for calling the PickandDependence obj's underlying member method code with example usage: obj.deriv2(t) and returns: d2