class_docstr: asymmetric logistic model of Tawn 1988
  
  special case: a1=a2=1 : Gumbel
  
  restrictions:
   - theta in (0,1]
   - a1, a2 in [0,1]
code: def deriv(self, t, a1, a2, theta):
      b = theta
  
      d1 = (
          (
              a1 * (a1 * t) ** (1 / b - 1)
              - a2 * (a2 * (1 - t)) ** (1 / b - 1)
          )
          * ((a1 * t) ** (1 / b) + (a2 * (1 - t)) ** (1 / b)) ** (b - 1)
          - a1
          + a2
      )
      return d1
  
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 AsymLogistic obj's underlying member method code with example usage: obj.deriv(t, a1, a2, theta) and returns: d1