class_docstr: t-EV model of Demarta and McNeil 2005
  
  restrictions:
   - rho in (-1,1)
   - x > 0
code: def evaluate(self, t, rho, df):
      x = df  # alias, Genest and Segers use chi, copual package uses df
      # if not np.all(self, _check_args(rho, x)):
      #    raise ValueError('invalid args')
  
      from scipy.stats import t as stats_t
  
      # use special if I want to avoid stats import
  
      term1 = np.power(t / (1.0 - t), 1.0 / x) - rho  # for t
      term2 = np.power((1.0 - t) / t, 1.0 / x) - rho  # for 1-t
      term0 = np.sqrt(1.0 + x) / np.sqrt(1 - rho * rho)
      z1 = term0 * term1
      z2 = term0 * term2
      transf = t * stats_t._cdf(z1, x + 1) + (1 - t) * stats_t._cdf(
          z2, x + 1
      )
      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 TEV obj's underlying member method code with example usage: obj.evaluate(t, rho, df) and returns: transf