code: def _check_loss_fn(
      influence_instance: Union[
          "TracInCPBase", "InfluenceFunctionBase"
      ],
      loss_fn: Optional[Union[Module, Callable]],
      loss_fn_name: str,
      sample_wise_grads_per_batch: Optional[bool] = None,
  ) -> str:
      """
      This checks whether `loss_fn` satisfies the requirements assumed of all
      implementations of `TracInCPBase`. It works regardless of whether the
      implementation has the `sample_wise_grads_per_batch` attribute.
      It returns the reduction type of the loss_fn. If `sample_wise_grads_per_batch`
      if not provided, we assume the implementation does not have that attribute.
      """
      # if `loss_fn` is `None`, there is nothing to check. then, the reduction type is
      # only used by `_compute_jacobian_wrt_params_with_sample_wise_trick`, where
      # reduction type should be "sum" if `loss_fn` is `None`.
      if loss_fn is None:
          return "sum"
  
      # perhaps since `Module` is an implementation of `Callable`, this has redundancy
      assert isinstance(loss_fn, Module) or callable(loss_fn)
  
      reduction_type = "none"
  
      # If we are able to access the reduction used by `loss_fn`, we check whether
      # the reduction is compatible with `sample_wise_grads_per_batch`, if it has the
      # attribute.
      if hasattr(loss_fn, "reduction"):
          reduction = loss_fn.reduction  # type: ignore
          if sample_wise_grads_per_batch is None:
              assert reduction in [
                  "sum",
                  "mean",
              ], 'reduction for `loss_fn` must be "sum" or "mean"'
              reduction_type = str(reduction)
          elif sample_wise_grads_per_batch:
              assert reduction in ["sum", "mean"], (
                  'reduction for `loss_fn` must be "sum" or "mean" when '
                  "`sample_wise_grads_per_batch` is True"
              )
              reduction_type = str(reduction)
          else:
              assert reduction == "none", (
                  'reduction for `loss_fn` must be "none" when '
                  "`sample_wise_grads_per_batch` is False"
              )
      else:
          # if we are unable to access the reduction used by `loss_fn`, we warn
          # the user about the assumptions we are making regarding the reduction
          # used by `loss_fn`
          if sample_wise_grads_per_batch is None:
              warnings.warn(
                  f'Since `{loss_fn_name}` has no "reduction" attribute, the '
                  f'implementation  assumes that `{loss_fn_name}` is a "reduction" loss '
                  "function that reduces the per-example losses by taking their *sum*. "
                  f"If `{loss_fn_name}` instead reduces the per-example losses by "
                  f"taking their mean, please set the reduction attribute of "
                  f'`{loss_fn_name}` to "mean", i.e. '
                  f'`{loss_fn_name}.reduction = "mean"`.'
              )
              reduction_type = "sum"
          elif sample_wise_grads_per_batch:
              warnings.warn(
                  f"Since `{loss_fn_name}`` has no 'reduction' attribute, and "
                  "`sample_wise_grads_per_batch` is True, the implementation assumes "
                  f"that `{loss_fn_name}` is a 'reduction' loss function that reduces "
                  f"the per-example losses by taking their *sum*. If `{loss_fn_name}` "
                  "instead reduces the per-example losses by taking their mean, "
                  f'please set the reduction attribute of `{loss_fn_name}` to "mean", '
                  f'i.e. `{loss_fn_name}.reduction = "mean"`. Note that if '
                  "`sample_wise_grads_per_batch` is True, the implementation "
                  "assumes the reduction is either a sum or mean reduction."
              )
              reduction_type = "sum"
          else:
              warnings.warn(
                  f'Since `{loss_fn_name}` has no "reduction" attribute, and '
                  "`sample_wise_grads_per_batch` is False, the implementation "
                  f'assumes that `{loss_fn_name}` is a "per-example" loss function (see '
                  f"documentation for `{loss_fn_name}` for details).  Please ensure "
                  "that this is the case."
              )
  
      return reduction_type
  
imports: import warnings
  from functools import reduce
  from typing import Any, Callable, Iterable, List, NamedTuple, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn
  from captum._utils.common import _get_module_from_name, _parse_version
  from captum._utils.gradient import _compute_jacobian_wrt_params, _compute_jacobian_wrt_params_with_sample_wise_trick
  from captum._utils.progress import progress
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 8 for calling a global function with example usage: _check_loss_fn(influence_instance, loss_fn, loss_fn_name, sample_wise_grads_per_batch) and returns: reduction_type, str