code: def _compute_batch_loss_influence_function_base(
      loss_fn: Optional[Union[Module, Callable]],
      input: Any,
      target: Any,
      reduction_type: str,
  ):
      """
      In implementations of `InfluenceFunctionBase`, we need to compute the total loss
      for a batch given `loss_fn`, whose reduction can either be 'none', 'sum', or
      'mean', and whose output requires different scaling based on the reduction. This
      helper houses that common logic, and returns the total loss for a batch given the
      predictions (`inputs`) and labels (`targets`) for it. We compute the total loss
      in order to compute the Hessian.
      """
      if loss_fn is not None:
          _loss = loss_fn(input, target)
      else:
          # following convention of `_compute_jacobian_wrt_params`, is no loss function is
          # provided, the quantity backpropped is the output of the forward function.
          assert reduction_type == "none"
          _loss = input
  
      if reduction_type == "none":
          # if loss_fn is a "reduction='none'" loss function, need to sum
          # up the per-example losses.
          return torch.sum(_loss)
      elif reduction_type == "mean":
          # in this case, we want the total loss for the batch, and should
          # multiply the mean loss for the batch by the batch size. however,
          # we can only infer the batch size if `_output` is a Tensor, and
          # we assume the 0-th dimension to be the batch dimension.
          if isinstance(input, Tensor):
              multiplier = input.shape[0]
          else:
              multiplier = 1
              msg = (
                  "`loss_fn` was inferred to behave as a `reduction='mean'` "
                  "loss function. however, the batch size of batches could not "
                  "be inferred. therefore, the total loss of a batch, which is "
                  "needed to compute the Hessian, is approximated as the output "
                  "of `loss_fn` for the batch. if this approximation is not "
                  "accurate, please change `loss_fn` to behave as a "
                  "`reduction='sum'` loss function, or a `reduction='none'` "
                  "and set `sample_grads_per_batch` to false."
              )
              warnings.warn(msg)
          return _loss * multiplier
      elif reduction_type == "sum":
          return _loss
      else:
          # currently, only support `reduction_type` to be
          # 'none', 'sum', or 'mean' for
          # `InfluenceFunctionBase` implementations
          raise Exception
  
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 26 for calling a global function with example usage: _compute_batch_loss_influence_function_base(loss_fn, input, target, reduction_type) and returns: torch, unknown, _loss