parameters: (self, x, grad_level=0)
docstring: Evaluates the constraint function and possibly (determining on
          grad_level) the Jacobian.
  
          Args:
              x (ndarray): the point to be evaluated
              grad_level (int, optional): which level of gradient is computed.
                  Defaults to 0.
  
          Returns:
              tuple: The constraint value and optional derivatives, structured as
              follows:
  
              - If grad_level == 0, it returns a 1-D ndarray of g(x)
              - If grad_level == 1, it also returns a 2-D ndarray giving the
                constraint Jacobian d/dx g(x).
docstring_summary: Evaluates the constraint function and possibly (determining on
          grad_level) the Jacobian.
docstring_tokens: Evaluates the constraint function and possibly ( determining on grad_level ) the Jacobian .
function: def compute(self, x, grad_level=0):
          """Evaluates the constraint function and possibly (determining on
          grad_level) the Jacobian.
  
          Args:
              x (ndarray): the point to be evaluated
              grad_level (int, optional): which level of gradient is computed.
                  Defaults to 0.
  
          Returns:
              tuple: The constraint value and optional derivatives, structured as
              follows:
  
              - If grad_level == 0, it returns a 1-D ndarray of g(x)
              - If grad_level == 1, it also returns a 2-D ndarray giving the
                constraint Jacobian d/dx g(x).
          """
          raise NotImplementedError("Sub-class has to implement function compute.")
function_tokens: def compute ( self , x , grad_level = 0 ) : raise NotImplementedError ( "Sub-class has to implement function compute." )