code: def scipy_minimize(
      closure: Union[
          Callable[[], Tuple[Tensor, Sequence[Optional[Tensor]]]],
          NdarrayOptimizationClosure,
      ],
      parameters: Dict[str, Tensor],
      bounds: Optional[
          Dict[str, Tuple[Optional[float], Optional[float]]]
      ] = None,
      callback: Optional[
          Callable[[Dict[str, Tensor], OptimizationResult], None]
      ] = None,
      x0: Optional[ndarray] = None,
      method: str = "L-BFGS-B",
      options: Optional[Dict[str, Any]] = None,
      timeout_sec: Optional[float] = None,
  ) -> OptimizationResult:
      r"""Generic scipy.optimize.minimize-based optimization routine.
  
      Args:
          closure: Callable that returns a tensor and an iterable of gradient tensors or
              NdarrayOptimizationClosure instance.
          parameters: A dictionary of tensors to be optimized.
          bounds: A dictionary mapping parameter names to lower and upper bounds.
          callback: A callable taking `parameters` and an OptimizationResult as arguments.
          x0: An optional initialization vector passed to scipy.optimize.minimize.
          method: Solver type, passed along to scipy.minimize.
          options: Dictionary of solver options, passed along to scipy.minimize.
          timeout_sec: Timeout in seconds to wait before aborting the optimization loop
              if not converged (will return the best found solution thus far).
  
      Returns:
          An OptimizationResult summarizing the final state of the run.
      """
      start_time = monotonic()
      wrapped_closure = (
          closure
          if isinstance(closure, NdarrayOptimizationClosure)
          else NdarrayOptimizationClosure(closure, parameters)
      )
      if bounds is None:
          bounds_np = None
      else:
          bounds_np = get_bounds_as_ndarray(parameters, bounds)
  
      if callback is None:
          wrapped_callback = None
      else:
          call_counter = count(
              1
          )  # callbacks are typically made at the end of each iter
  
          def wrapped_callback(x: ndarray):
              result = OptimizationResult(
                  step=next(call_counter),
                  fval=float(wrapped_closure(x)[0]),
                  status=OptimizationStatus.RUNNING,
                  runtime=monotonic() - start_time,
              )
              return callback(parameters, result)  # pyre-ignore [29]
  
      raw = minimize_with_timeout(
          wrapped_closure,
          wrapped_closure.state
          if x0 is None
          else x0.astype(np_float64, copy=False),
          jac=True,
          bounds=bounds_np,
          method=method,
          options=options,
          callback=wrapped_callback,
          timeout_sec=timeout_sec,
      )
  
      # Post-processing and outcome handling
      wrapped_closure.state = asarray(
          raw.x
      )  # set parameter state to optimal values
      msg = (
          raw.message
          if isinstance(raw.message, str)
          else raw.message.decode("ascii")
      )
      if raw.success:
          status = OptimizationStatus.SUCCESS
      else:
          status = (  # Check whether we stopped due to reaching maxfun or maxiter
              OptimizationStatus.STOPPED
              if _LBFGSB_MAXITER_MAXFUN_REGEX.search(msg)
              or "Optimization timed out after" in msg
              else OptimizationStatus.FAILURE
          )
  
      return OptimizationResult(
          fval=raw.fun,
          step=raw.nit,
          status=status,
          message=msg,
          runtime=monotonic() - start_time,
      )
  
imports: from __future__ import annotations
  import re
  from dataclasses import dataclass, replace
  from enum import auto, Enum
  from itertools import count
  from sys import maxsize
  from time import monotonic
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  from botorch.optim.closures import NdarrayOptimizationClosure
  from botorch.optim.utils.numpy_utils import get_bounds_as_ndarray
  from botorch.optim.utils.timeout import minimize_with_timeout
  from numpy import asarray, float64, ndarray
  from torch import Tensor
  from torch.optim.adam import Adam
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 1 for calling a global function with example usage: scipy_minimize(closure, parameters, bounds, callback, x0, method, options, timeout_sec) and returns: OptimizationResult, callback