code: def minimize_with_timeout(
      fun: Callable[[np.ndarray, *Any], float],
      x0: np.ndarray,
      args: Tuple[Any, ...] = (),
      method: Optional[str] = None,
      jac: Optional[Union[str, Callable, bool]] = None,
      hess: Optional[
          Union[str, Callable, optimize.HessianUpdateStrategy]
      ] = None,
      hessp: Optional[Callable] = None,
      bounds: Optional[
          Union[Sequence[Tuple[float, float]], optimize.Bounds]
      ] = None,
      constraints=(),  # Typing this properly is a s**t job
      tol: Optional[float] = None,
      callback: Optional[Callable] = None,
      options: Optional[Dict[str, Any]] = None,
      timeout_sec: Optional[float] = None,
  ) -> optimize.OptimizeResult:
      r"""Wrapper around scipy.optimize.minimize to support timeout.
  
      This method calls scipy.optimize.minimize with all arguments forwarded
      verbatim. The only difference is that if provided a `timeout_sec` argument,
      it will automatically stop the optimziation after the timeout is reached.
  
      Internally, this is achieved by automatically constructing a wrapper callback
      method that is injected to the scipy.optimize.minimize call and that keeps
      track of the runtime and the optimization variables at the current iteration.
      """
      if timeout_sec:
          start_time = time.monotonic()
          callback_data = {
              "num_iterations": 0
          }  # update from withing callback below
  
          def timeout_callback(xk: np.ndarray) -> bool:
              runtime = time.monotonic() - start_time
              callback_data["num_iterations"] += 1
              if runtime > timeout_sec:
                  raise OptimizationTimeoutError(
                      current_x=xk, runtime=runtime
                  )
              return False
  
          if callback is None:
              wrapped_callback = timeout_callback
  
          elif callable(method):
              raise NotImplementedError(
                  "Custom callable not supported for `method` argument."
              )
  
          elif method == "trust-constr":  # special signature
  
              def wrapped_callback(
                  xk: np.ndarray, state: optimize.OptimizeResult
              ) -> bool:
                  # order here is important to make sure base callback gets executed
                  return callback(xk, state) or timeout_callback(xk=xk)
  
          else:
  
              def wrapped_callback(xk: np.ndarray) -> None:
                  timeout_callback(xk=xk)
                  callback(xk)
  
      else:
          wrapped_callback = callback
  
      try:
          return optimize.minimize(
              fun=fun,
              x0=x0,
              args=args,
              method=method,
              jac=jac,
              hess=hess,
              hessp=hessp,
              bounds=bounds,
              constraints=constraints,
              tol=tol,
              callback=wrapped_callback,
              options=options,
          )
      except OptimizationTimeoutError as e:
          msg = f"Optimization timed out after {e.runtime} seconds."
          current_fun, *_ = fun(e.current_x, *args)
  
          return optimize.OptimizeResult(
              fun=current_fun,
              x=e.current_x,
              nit=callback_data["num_iterations"],
              success=False,  # same as when maxiter is reached
              status=1,  # same as when L-BFGS-B reaches maxiter
              message=msg,
          )
  
imports: from __future__ import annotations
  import time
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
  import numpy
  from botorch.exceptions.errors import OptimizationTimeoutError
  from scipy import optimize
label_desc: Use image node_id 1 for calling a global function with example usage: minimize_with_timeout(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options, timeout_sec) and returns: optimize, False, optimize, unknown