code: def test_make_scipy_nonlinear_inequality_constraints(self):
      def nlc(x):
          return 4 - x.sum()
  
      def f_np_wrapper(x: np.ndarray, f: Callable):
          """Given a torch callable, compute value + grad given a numpy array."""
          X = (
              torch.from_numpy(x)
              .to(self.device)
              .view(shapeX)
              .contiguous()
              .requires_grad_(True)
          )
          loss = f(X).sum()
          # compute gradient w.r.t. the inputs (does not accumulate in leaves)
          gradf = _arrayify(
              torch.autograd.grad(loss, X)[0].contiguous().view(-1)
          )
          fval = loss.item()
          return fval, gradf
  
      shapeX = torch.Size((3, 2, 4))
      b, q, _ = shapeX
      x = torch.ones(shapeX.numel(), device=self.device)
  
      with self.assertRaisesRegex(
          ValueError,
          f"A nonlinear constraint has to be a tuple, got {type(nlc)}.",
      ):
          make_scipy_nonlinear_inequality_constraints(
              [nlc], f_np_wrapper, x, shapeX
          )
      with self.assertRaisesRegex(
          ValueError,
          "A nonlinear constraint has to be a tuple of length 2, got length 1.",
      ):
          make_scipy_nonlinear_inequality_constraints(
              [(nlc,)], f_np_wrapper, x, shapeX
          )
      with self.assertRaisesRegex(
          ValueError,
          "`batch_initial_conditions` must satisfy the non-linear inequality "
          "constraints.",
      ):
          make_scipy_nonlinear_inequality_constraints(
              [(nlc, False)], f_np_wrapper, x, shapeX
          )
      # empty list
      res = make_scipy_nonlinear_inequality_constraints(
          [], f_np_wrapper, x, shapeX
      )
      self.assertEqual(res, [])
      # only inter
      x = torch.zeros(shapeX.numel(), device=self.device)
      res = make_scipy_nonlinear_inequality_constraints(
          [(nlc, False)], f_np_wrapper, x, shapeX
      )
      self.assertEqual(len(res), b)
      # only intra
      res = make_scipy_nonlinear_inequality_constraints(
          [(nlc, True)], f_np_wrapper, x, shapeX
      )
      self.assertEqual(len(res), b * q)
      # intra and inter
      res = make_scipy_nonlinear_inequality_constraints(
          [(nlc, True), (nlc, False)], f_np_wrapper, x, shapeX
      )
      self.assertEqual(len(res), b * q + b)
  
imports: from itertools import product
  from typing import Callable
  import numpy
  import torch
  from botorch.exceptions.errors import CandidateGenerationError, UnsupportedError
  from botorch.optim.parameter_constraints import _arrayify, _generate_unfixed_lin_constraints, _generate_unfixed_nonlin_constraints, _make_linear_constraints, _make_nonlinear_constraints, eval_lin_constraint, lin_constraint_jac, make_scipy_bounds, make_scipy_linear_constraints, make_scipy_nonlinear_inequality_constraints, nonlinear_constraint_is_feasible
  from botorch.utils.testing import BotorchTestCase
  from scipy.optimize import Bounds
label_desc: Use image node_id 5 for calling the TestParameterConstraints obj's underlying member method code with example usage: obj.test_make_scipy_nonlinear_inequality_constraints() and returns: unknown, fval,  gradf