class_docstr: Base class for Archimedean copulas
  
  Parameters
  ----------
  transform : instance of transformation class
      Archimedean generator 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
      Dimension, number of components in the multivariate random variable.
      Currently only bivariate copulas are verified. Support for more than
      2 dimension is incomplete.
class_docstr_tok: ["Base","class","for","Archimedean","copulas","Parameters","--","--","--","--","--","transform",":","instance","of","transformation","class","Archimedean","generator","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","Dimension",",","number","of","components","in","the","multivariate","random","variable",".","Currently","only","bivariate","copulas","are","verified",".","Support","for","more","than","2","dimension","is","incomplete","."]
code: def _handle_u(self, u):
      u = np.asarray(u)
      if u.shape[-1] != self.k_dim:
          import warnings
  
          warnings.warn(
              "u has different dimension than k_dim. "
              "This will raise exception in future versions",
              FutureWarning,
          )
  
      return u
  
imports: import sys
  import numpy
  from scipy import stats, integrate, optimize
  from .None import transforms
  from .copulas import Copula
  from statsmodels.tools.rng_qrng import check_random_state
label_desc: Use image node_id 3 for calling the ArchimedeanCopula obj's underlying member method code with example usage: obj._handle_u(u) and returns: u