parameters: (self,arg,*args)
docstring: Generalized derivative that allows higher-order derivatives to be
          taken.
  
          Args:
              arg (list): Indicates the order of derivatives to be taken.  For
                  example, to take the 2nd derivative w.r.t. x0,x1,
                  arg = [0,1] should be specified.
              args (list of ndarrays): arguments for the function.
  
          Returns:
              ndarray: A tensor of shape ``(n_out,n_in(arg[0]),...,
              n_in(arg[-1])``.
  
              If the generalized derivative is not implemented, raise a
              NotImplementedError.
  
              If the generalized derivative is zero, can just return 0 (the
              integer) regardless of the size of the result.
docstring_summary: Generalized derivative that allows higher-order derivatives to be
          taken.
docstring_tokens: Generalized derivative that allows higher - order derivatives to be taken .
function: def gen_derivative(self,arg,*args):
          """Generalized derivative that allows higher-order derivatives to be
          taken.
  
          Args:
              arg (list): Indicates the order of derivatives to be taken.  For
                  example, to take the 2nd derivative w.r.t. x0,x1,
                  arg = [0,1] should be specified.
              args (list of ndarrays): arguments for the function.
  
          Returns:
              ndarray: A tensor of shape ``(n_out,n_in(arg[0]),...,
              n_in(arg[-1])``.
  
              If the generalized derivative is not implemented, raise a
              NotImplementedError.
  
              If the generalized derivative is zero, can just return 0 (the
              integer) regardless of the size of the result.
          """
          raise NotImplementedError()
function_tokens: def gen_derivative ( self , arg , * args ) : raise NotImplementedError ( )