code: def get_loss_closure_with_grads(
      mll: MarginalLogLikelihood,
      parameters: Dict[str, Tensor],
      data_loader: Optional[DataLoader] = None,
      backward: Callable[[Tensor], None] = Tensor.backward,
      reducer: Optional[Callable[[Tensor], Tensor]] = Tensor.sum,
      context_manager: Optional[Callable] = None,
      **kwargs: Any,
  ) -> Callable[[], Tuple[Tensor, Tuple[Tensor, ...]]]:
      r"""Public API for GetLossClosureWithGrads dispatcher.
  
      In most cases, this method simply adds a backward pass to a loss closure obtained by
      calling `get_loss_closure`. For further details, see `get_loss_closure`.
  
      Args:
          mll: A MarginalLogLikelihood instance whose negative defines the loss.
          parameters: A dictionary of tensors whose `grad` fields are to be returned.
          reducer: Optional callable used to reduce the output of the forward pass.
          data_loader: An optional DataLoader instance for cases where training
              data is passed in rather than obtained from `mll.model`.
          context_manager: An optional ContextManager used to wrap each forward-backward
              pass. Defaults to a `zero_grad_ctx` that zeroes the gradients of
              `parameters` upon entry. None may be passed as an alias for `nullcontext`.
  
      Returns:
          A closure that takes zero positional arguments and returns the reduced and
          negated value of `mll` along with the gradients of `parameters`.
      """
      return GetLossClosureWithGrads(
          mll,
          type(mll.likelihood),
          type(mll.model),
          data_loader,
          parameters=parameters,
          reducer=reducer,
          backward=backward,
          context_manager=context_manager,
          **kwargs,
      )
  
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 2 for calling a global function with example usage: get_loss_closure_with_grads(mll, parameters, data_loader, backward, reducer, context_manager) and returns: GetLossClosureWithGrads