code: def _get_k_most_influential_helper(
      influence_src_dataloader: DataLoader,
      influence_batch_fn: Callable,
      inputs: Any,
      k: int = 5,
      proponents: bool = True,
      show_progress: bool = False,
      desc: Optional[str] = None,
  ) -> Tuple[Tensor, Tensor]:
      r"""
      Helper function that computes the quantities returned by
      `TracInCPBase._get_k_most_influential`, using a specific implementation that is
      constant memory.
  
      Args:
          influence_src_dataloader (DataLoader): The DataLoader, representing training
                  data, for which we want to compute proponents / opponents.
          influence_batch_fn (Callable): A callable that will be called via
                  `influence_batch_fn(inputs, batch)`, where `batch` is a batch
                  in the `influence_src_dataloader` argument.
          inputs (any): This argument represents the test batch, and can be of any type.
                  It is passed as the first argument to `influence_batch_fn`, and thus
                  needs to be compatible with it. It is not necessarily the test batch
                  itself, but can be some quantity derived from it, i.e. its jacobians.
          k (int, optional): The number of proponents or opponents to return per test
                  instance.
                  Default: 5
          proponents (bool, optional): Whether seeking proponents (`proponents=True`)
                  or opponents (`proponents=False`)
                  Default: True
          show_progress (bool, optional): To compute the proponents (or opponents)
                  for the batch of examples, we perform computation for each batch in
                  training dataset `influence_src_dataloader`, If `show_progress` is
                  true, the progress of this computation will be displayed. In
                  particular, the number of batches for which the computation has
                  been performed will be displayed. It will try to use tqdm if
                  available for advanced features (e.g. time estimation). Otherwise,
                  it will fallback to a simple output of progress.
                  Default: False
          desc (str, optional): If `show_progress` is true, this is the description to
                  show when displaying progress. If `desc` is none, no description is
                  shown.
                  Default: None
  
      Returns:
          (indices, influence_scores): `indices` is a torch.long Tensor that contains the
                  indices of the proponents (or opponents) for each test example. Its
                  dimension is `(inputs_batch_size, k)`, where `inputs_batch_size` is the
                  number of examples in `inputs`. For example, if `proponents==True`,
                  `indices[i][j]` is the index of the example in training dataset
                  `influence_src_dataloader` with the k-th highest influence score for
                  the j-th example in `inputs`. `indices` is a `torch.long` tensor so that
                  it can directly be used to index other tensors. Each row of
                  `influence_scores` contains the influence scores for a different test
                  example, in sorted order. In particular, `influence_scores[i][j]` is
                  the influence score of example `indices[i][j]` in training dataset
                  `influence_src_dataloader` on example `i` in the test batch represented
                  by `inputs` and `targets`.
      """
      # For each test instance, maintain the best indices and corresponding distances
      # initially, these will be empty
      topk_indices = torch.Tensor().long()
      topk_tracin_scores = torch.Tensor()
  
      multiplier = 1.0 if proponents else -1.0
  
      # needed to map from relative index in a batch fo index within entire `dataloader`
      num_instances_processed = 0
  
      # if show_progress, create progress bar
      total: Optional[int] = None
      if show_progress:
          try:
              total = len(influence_src_dataloader)
          except AttributeError:
              pass
          influence_src_dataloader = progress(
              influence_src_dataloader,
              desc=desc,
              total=total,
          )
  
      for batch in influence_src_dataloader:
          # calculate tracin_scores for the batch
          batch_tracin_scores = influence_batch_fn(inputs, batch)
          batch_tracin_scores *= multiplier
  
          # get the top-k indices and tracin_scores for the batch
          batch_size = batch_tracin_scores.shape[1]
          batch_topk_tracin_scores, batch_topk_indices = torch.topk(
              batch_tracin_scores, min(batch_size, k), dim=1
          )
          batch_topk_indices = (
              batch_topk_indices + num_instances_processed
          )
          num_instances_processed += batch_size
  
          # combine the top-k for the batch with those for previously seen batches
          topk_indices = torch.cat(
              [
                  topk_indices.to(batch_topk_indices.device),
                  batch_topk_indices,
              ],
              dim=1,
          )
          topk_tracin_scores = torch.cat(
              [
                  topk_tracin_scores.to(
                      batch_topk_tracin_scores.device
                  ),
                  batch_topk_tracin_scores,
              ],
              dim=1,
          )
  
          # retain only the top-k in terms of tracin_scores
          topk_tracin_scores, topk_argsort = torch.topk(
              topk_tracin_scores, min(k, topk_indices.shape[1]), dim=1
          )
          topk_indices = torch.gather(
              topk_indices, dim=1, index=topk_argsort
          )
  
      # if seeking opponents, we were actually keeping track of negative tracin_scores
      topk_tracin_scores *= multiplier
  
      return topk_indices, topk_tracin_scores
  
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 5 for calling a global function with example usage: _get_k_most_influential_helper(influence_src_dataloader, influence_batch_fn, inputs, k, proponents, show_progress, desc) and returns: topk_indices,  topk_tracin_scores