class_docstr: Extreme value copula constructed from Pickand's dependence function.
  
  Currently only bivariate copulas are available.
  
  Parameters
  ----------
  transform: instance of transformation class
      Pickand's dependence function with required methods including first
      and second derivatives
  args : tuple
      Optional copula parameters. Copula parameters can be either provided
      when creating the instance or as arguments when calling methods.
  k_dim : int
      Currently only bivariate extreme value copulas are supported.
  
  Notes
  -----
  currently the following dependence function and copulas are available
  
  - AsymLogistic
  - AsymNegLogistic
  - AsymMixed
  - HR
  
  TEV and AsymBiLogistic currently do not have required derivatives for pdf.
  
  See Also
  --------
  dep_func_ev
docstr: conditional distribution
  
  not yet implemented
  
  C2|1(u2|u1) := ∂C(u1, u2) / ∂u1 = C(u1, u2) / u1 * (A(t) − t A'(t))
  
  where t = np.log(v)/np.log(u*v)
code: def conditional_2g1(self, u, args=()):
      """conditional distribution
  
      not yet implemented
  
      C2|1(u2|u1) := ∂C(u1, u2) / ∂u1 = C(u1, u2) / u1 * (A(t) − t A'(t))
  
      where t = np.log(v)/np.log(u*v)
      """
      raise NotImplementedError
  
file_path: statsmodels/statsmodels/distributions/copula/extreme_value.py
imports: import numpy
  from .copulas import Copula
label_desc: Use image node_id 6 for calling the ExtremeValueCopula obj's underlying member method code with example usage: obj.conditional_2g1(u, args) without return types