class_docstr: Clayton copula.
  
  Dependence is greater in the negative tail than in the positive.
  
  .. math::
  
      C_\theta(u,v) = \left[ \max\left\{ u^{-\theta} + v^{-\theta} -1 ;
      0 \right\} \right]^{-1/\theta}
  
  with :math:`\theta\in[-1,\infty)\backslash\{0\}`.
class_docstr_tok: ["Clayton","copula",".","Dependence","is","greater","in","the","negative","tail","than","in","the","positive",".","..","math",":",":","C_\\theta","(","u",",","v",")","=","\\left","[","\\max\\left\\","{","u^","{","-\\theta","}","+","v^","{","-\\theta","}","-1",";","0","\\right\\","}","\\right","]","^","{","-1\/\\theta","}","with",":","math",":","`","\\theta\\in","[","-1",",","\\infty",")","\\backslash\\","{","0\\","}","`","."]
code: def pdf(self, u, args=()):
      u = self._handle_u(u)
      (th,) = self._handle_args(args)
      if u.shape[-1] == 2:
          a = (th + 1) * np.prod(u, axis=-1) ** -(th + 1)
          b = np.sum(u**-th, axis=-1) - 1
          c = -(2 * th + 1) / th
          return a * b**c
      else:
          return super().pdf(u, args)
  
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 ClaytonCopula obj's underlying member method code with example usage: obj.pdf(u, args) and returns: unknown, super