code: def _scipy_objective_and_grad(
      x: np.ndarray,
      mll: ExactMarginalLogLikelihood,
      property_dict: Dict[str, TorchAttr],
  ) -> Tuple[Union[float, np.ndarray], np.ndarray]:
      r"""Get objective and gradient in format that scipy expects.
  
  
      NOTE: `_scipy_objective_and_grad` was originally a BoTorch function and was later
      deprecated. It has been copied here because ALEBO depends on it, and because
      ALEBO itself is deprecated, it is not worth moving ALEBO to the new syntax.
  
      Args:
          x: The (flattened) input parameters.
          mll: The MarginalLogLikelihood module to evaluate.
          property_dict: The property dictionary required to "unflatten" the input
              parameter vector, as generated by `module_to_array`.
  
      Returns:
          2-element tuple containing
  
          - The objective value.
          - The gradient of the objective.
      """
      mll = set_params_with_array(mll, x, property_dict)
      train_inputs, train_targets = (
          mll.model.train_inputs,
          mll.model.train_targets,
      )
      mll.zero_grad()
      try:  # catch linear algebra errors in gpytorch
          output = mll.model(*train_inputs)
          args = [output, train_targets] + list(mll.model.train_inputs)
          # pyre-fixme[16]: Undefined attribute. Item
          # `torch.distributions.distribution.Distribution` of
          # `typing.Union[linear_operator.operators._linear_operator.LinearOperator,
          # torch._tensor.Tensor, torch.distributions.distribution.Distribution]`
          # has no attribute `sum`.
          loss = -mll(*args).sum()
      except RuntimeError as e:
          return _handle_numerical_errors(error=e, x=x)
      loss.backward()
  
      i = 0
      param_dict = OrderedDict(mll.named_parameters())
      grad = np.zeros(
          sum([tattr.shape.numel() for tattr in property_dict.values()])
      )
      for p_name in property_dict:
          t = param_dict[p_name]
          size = t.numel()
          t_grad = t.grad
          if t.requires_grad and t_grad is not None:
              grad[i : i + size] = (
                  t_grad.detach()
                  .view(-1)
                  .cpu()
                  .double()
                  .clone()
                  .numpy()
              )
          i += size
  
      mll.zero_grad()
      return loss.item(), grad
  
imports: from __future__ import annotations
  import dataclasses
  import re
  from collections import OrderedDict
  from logging import Logger
  from math import inf
  from numbers import Number
  from typing import Any, Callable, Dict, List, MutableMapping, Optional, Tuple, TypeVar, Union
  from warnings import warn
  import gpytorch
  import numpy
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.core.types import TCandidateMetadata
  from ax.models.random.alebo_initializer import ALEBOInitializer
  from ax.models.torch.botorch import BotorchModel
  from ax.models.torch.botorch_defaults import get_qLogNEI
  from ax.models.torch.utils import _datasets_to_legacy_inputs
  from ax.models.torch_base import TorchGenResults, TorchModel, TorchOptConfig
  from ax.utils.common.docutils import copy_doc
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.analytic import ExpectedImprovement
  from botorch.acquisition.objective import PosteriorTransform
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.optim.fit import fit_gpytorch_mll_scipy
  from botorch.optim.initializers import initialize_q_batch_nonneg
  from botorch.optim.optimize import optimize_acqf
  from botorch.optim.utils import _handle_numerical_errors, get_parameters_and_bounds, TorchAttr
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.utils.datasets import SupervisedDataset
  from gpytorch.distributions.multivariate_normal import MultivariateNormal
  from gpytorch.kernels.kernel import Kernel
  from gpytorch.kernels.rbf_kernel import postprocess_rbf
  from gpytorch.kernels.scale_kernel import ScaleKernel
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from scipy.optimize import approx_fprime
  from torch import Tensor
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 3 for calling a global function with example usage: _scipy_objective_and_grad(x, mll, property_dict) and returns: loss,  grad, _handle_numerical_errors