class_docstr: bilogistic model of Coles and Tawn 1994, Joe, Smith and Weissman 1992
  
  restrictions:
   - (beta, delta) in (0,1)^2 or
   - (beta, delta) in (-inf,0)^2
  
  not vectorized because of numerical integration
code: def evaluate(self, t, beta, delta):
      # if not np.all(_check_args(beta, delta)):
      #    raise ValueError('invalid args')
  
      def _integrant(w):
          term1 = (1 - beta) * np.power(w, -beta) * (1 - t)
          term2 = (1 - delta) * np.power(1 - w, -delta) * t
          return np.maximum(term1, term2)
  
      from scipy.integrate import quad
  
      transf = quad(_integrant, 0, 1)[0]
      return transf
  
imports: import numpy
  from scipy import stats
  from statsmodels.tools.numdiff import _approx_fprime_cs_scalar, approx_hess
label_desc: Use image node_id 2 for calling the AsymBiLogistic obj's underlying member method code with example usage: obj.evaluate(t, beta, delta) and returns: transf, np