code: def _get_loss_closure_fallback_external(
      mll: MarginalLogLikelihood,
      _: object,
      __: object,
      data_loader: DataLoader,
      **ignore: Any,
  ) -> Callable[[], Tensor]:
      r"""Fallback loss closure with externally provided data."""
      batch_generator = chain.from_iterable(
          iter(data_loader) for _ in repeat(None)
      )
  
      def closure(**kwargs: Any) -> Tensor:
          batch = next(batch_generator)
          if not isinstance(batch, Sequence):
              raise TypeError(
                  "Expected `data_loader` to generate a batch of tensors, "
                  f"but found {type(batch)}."
              )
  
          num_inputs = len(mll.model.train_inputs)
          model_output = mll.model(*batch[:num_inputs])
          log_likelihood = mll(
              model_output, *batch[num_inputs:], **kwargs
          )
          return -log_likelihood
  
      return closure
  
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 4 for calling a global function with example usage: _get_loss_closure_fallback_external(mll, _, __, data_loader) and returns: closure, unknown