code: def _get_loss_closure_with_grads_fallback(
      mll: MarginalLogLikelihood,
      _: object,
      __: object,
      data_loader: Optional[DataLoader],
      parameters: Dict[str, Tensor],
      reducer: Callable[[Tensor], Tensor] = Tensor.sum,
      backward: Callable[[Tensor], None] = Tensor.backward,
      context_manager: Callable = None,  # pyre-ignore [9]
      **kwargs: Any,
  ) -> ForwardBackwardClosure:
      r"""Wraps a `loss_closure` with a ForwardBackwardClosure."""
      loss_closure = get_loss_closure(
          mll, data_loader=data_loader, **kwargs
      )
      return ForwardBackwardClosure(
          forward=loss_closure,
          backward=backward,
          parameters=parameters,
          reducer=reducer,
          context_manager=context_manager,
      )
  
file_path: botorch/botorch/optim/closures/model_closures.py
imports: from __future__ import annotations
  from itertools import chain, repeat
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple
  from botorch.optim.closures.core import ForwardBackwardClosure
  from botorch.utils.dispatcher import Dispatcher, type_bypassing_encoder
  from botorch.utils.types import NoneType
  from gpytorch.mlls import ExactMarginalLogLikelihood, MarginalLogLikelihood, SumMarginalLogLikelihood
  from torch import Tensor
  from torch.utils.data import DataLoader
label_desc: Use image node_id 3 for calling a global function with example usage: _get_loss_closure_with_grads_fallback(mll, _, __, data_loader, parameters, reducer, backward, context_manager) and returns: ForwardBackwardClosure