class_docstr: model of Huesler Reiss 1989
  
  special case:  a1=a2=1 : symmetric negative logistic of Galambos 1978
  
  restrictions:
   - lambda in (0,inf)
code: def _derivs(self, t, lamda, order=(1, 2)):
      if not isinstance(order, (int, np.integer)):
          if (1 in order) and (2 in order):
              order = -1
          else:
              raise ValueError("order should be 1, 2, or (1,2)")
  
      dn = 1 / np.sqrt(2 * np.pi)
      a = lamda
      g = np.log((1.0 - t) / t) * 0.5 / a
      gd1 = 1 / (2 * a * (t - 1) * t)
      gd2 = (0.5 - t) / (a * ((1 - t) * t) ** 2)
      # f = stats.norm.cdf(t)
      # fd1 = np.exp(-t**2 / 2) / sqrt(2 * np.pi)  # stats.norm.pdf(t)
      # fd2 = fd1 * t
      tp = a + g
      fp = stats.norm.cdf(tp)
      fd1p = np.exp(-(tp**2) / 2) * dn  # stats.norm.pdf(t)
      fd2p = -fd1p * tp
      tn = a - g
      fn = stats.norm.cdf(tn)
      fd1n = np.exp(-(tn**2) / 2) * dn  # stats.norm.pdf(t)
      fd2n = -fd1n * tn
  
      if order in (1, -1):
          # d1 = g'(t) (-t f'(a - g(t)) - (t - 1) f'(a + g(t))) + f(a - g(t))
          #      - f(a + g(t))
          d1 = gd1 * (-t * fd1n - (t - 1) * fd1p) + fn - fp
      if order in (2, -1):
          # d2 = g'(t)^2 (t f''(a - g(t)) - (t - 1) f''(a + g(t))) +
          #     (-(t - 1) g''(t) - 2 g'(t)) f'(a + g(t)) -
          #     (t g''(t) + 2 g'(t)) f'(a - g(t))
          d2 = (
              gd1**2 * (t * fd2n - (t - 1) * fd2p)
              + (-(t - 1) * gd2 - 2 * gd1) * fd1p
              - (t * gd2 + 2 * gd1) * fd1n
          )
  
      if order == 1:
          return d1
      elif order == 2:
          return d2
      elif order == -1:
          return (d1, 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 3 for calling the HR obj's underlying member method code with example usage: obj._derivs(t, lamda, order) and returns: d1, d2, d1,  d2