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
code: def _handle_args(self, args):
      # TODO: how to we handle non-tuple args? two we allow single values?
      # Model fit might give an args that can be empty
      if isinstance(args, np.ndarray):
          args = tuple(args)  # handles empty arrays, unpacks otherwise
      if args == () or args is None:
          args = self.args
      if not isinstance(args, tuple):
          args = (args,)
  
      return args
  
file_path: statsmodels/statsmodels/distributions/copula/extreme_value.py
imports: import numpy
  from .copulas import Copula
label_desc: Use image node_id 2 for calling the ExtremeValueCopula obj's underlying member method code with example usage: obj._handle_args(args) and returns: args