code: def test_make_nonlinear_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, d = shapeX
      x = np.random.rand(shapeX.numel())
      # intra
      constraints = _make_nonlinear_constraints(
          f_np_wrapper=f_np_wrapper,
          nlc=nlc,
          is_intrapoint=True,
          shapeX=shapeX,
      )
      self.assertEqual(len(constraints), b * q)
      self.assertTrue(
          all(
              set(c.keys()) == {"fun", "jac", "type"}
              for c in constraints
          )
      )
      self.assertTrue(all(c["type"] == "ineq" for c in constraints))
      self.assertEqual(constraints[0]["fun"](x), 4.0 - x[:d].sum())
      self.assertEqual(
          constraints[1]["fun"](x), 4.0 - x[d : 2 * d].sum()
      )
      jac_exp = np.zeros(shapeX.numel())
      jac_exp[:4] = -1
      self.assertTrue(np.allclose(constraints[0]["jac"](x), jac_exp))
      jac_exp = np.zeros(shapeX.numel())
      jac_exp[4:8] = -1
      self.assertTrue(np.allclose(constraints[1]["jac"](x), jac_exp))
      # inter
      constraints = _make_nonlinear_constraints(
          f_np_wrapper=f_np_wrapper,
          nlc=nlc,
          is_intrapoint=False,
          shapeX=shapeX,
      )
      self.assertEqual(len(constraints), 3)
      self.assertTrue(
          all(
              set(c.keys()) == {"fun", "jac", "type"}
              for c in constraints
          )
      )
      self.assertTrue(all(c["type"] == "ineq" for c in constraints))
      self.assertTrue(
          np.allclose(constraints[0]["fun"](x), 4.0 - x[: q * d].sum())
      )
      self.assertTrue(
          np.allclose(
              constraints[1]["fun"](x), 4.0 - x[q * d : 2 * q * d].sum()
          )
      )
      jac_exp = np.zeros(shapeX.numel())
      jac_exp[: q * d] = -1.0
      self.assertTrue(np.allclose(constraints[0]["jac"](x), jac_exp))
      jac_exp = np.zeros(shapeX.numel())
      jac_exp[q * d : 2 * q * d] = -1.0
      self.assertTrue(np.allclose(constraints[1]["jac"](x), jac_exp))
  
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 4 for calling the TestParameterConstraints obj's underlying member method code with example usage: obj.test_make_nonlinear_constraints() and returns: unknown, fval,  gradf