code: def make_scipy_nonlinear_inequality_constraints(
      nonlinear_inequality_constraints: List[Tuple[Callable, bool]],
      f_np_wrapper: Callable,
      x0: Tensor,
      shapeX: torch.Size,
  ) -> List[Dict]:
      r"""Generate Scipy nonlinear inequality constraints from callables.
  
      Args:
          nonlinear_inequality_constraints: A list of tuples representing the nonlinear
              inequality constraints. The first element in the tuple is a callable
              representing a constraint of the form `callable(x) >= 0`. In case of an
              intra-point constraint, `callable()`takes in an one-dimensional tensor of
              shape `d` and returns a scalar. In case of an inter-point constraint,
              `callable()` takes a two dimensional tensor of shape `q x d` and again
              returns a scalar. The second element is a boolean, indicating if it is an
              intra-point or inter-point constraint (`True` for intra-point. `False` for
              inter-point). For more information on intra-point vs inter-point
              constraints, see the docstring of the `inequality_constraints` argument to
              `optimize_acqf()`. The constraints will later be passed to the scipy
              solver.
          f_np_wrapper: A wrapper function that given a constraint evaluates the value
               and gradient (using autograd) of a numpy input and returns both the
               objective and the gradient.
          x0: The starting point for SLSQP. We return this starting point in (rare)
              cases where SLSQP fails and thus require it to be feasible.
          shapeX: Shape of the three-dimensional batch X, that should be optimized.
  
      Returns:
          A list of dictionaries containing callables for constraint function
          values and Jacobians and a string indicating the associated constraint
          type ("eq", "ineq"), as expected by `scipy.minimize`.
      """
  
      scipy_nonlinear_inequality_constraints = []
      for constraint in nonlinear_inequality_constraints:
          if not isinstance(constraint, tuple):
              raise ValueError(
                  f"A nonlinear constraint has to be a tuple, got {type(constraint)}."
              )
          if len(constraint) != 2:
              raise ValueError(
                  "A nonlinear constraint has to be a tuple of length 2, "
                  f"got length {len(constraint)}."
              )
          nlc, is_intrapoint = constraint
          if not nonlinear_constraint_is_feasible(
              nlc, is_intrapoint=is_intrapoint, x=x0.reshape(shapeX)
          ):
              raise ValueError(
                  "`batch_initial_conditions` must satisfy the non-linear inequality "
                  "constraints."
              )
  
          scipy_nonlinear_inequality_constraints += (
              _make_nonlinear_constraints(
                  f_np_wrapper=f_np_wrapper,
                  nlc=nlc,
                  is_intrapoint=is_intrapoint,
                  shapeX=shapeX,
              )
          )
      return scipy_nonlinear_inequality_constraints
  
imports: from __future__ import annotations
  from functools import partial
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from botorch.exceptions.errors import CandidateGenerationError, UnsupportedError
  from scipy.optimize import Bounds
  from torch import Tensor
label_desc: Use image node_id 14 for calling a global function with example usage: make_scipy_nonlinear_inequality_constraints(nonlinear_inequality_constraints, f_np_wrapper, x0, shapeX) and returns: scipy_nonlinear_inequality_constraints