class_docstr: To implement the `influence` method, classes inheriting from `TracInCPBase` will
  separately implement the private `_self_influence`, `_get_k_most_influential`,
  and `_influence` methods. The public `influence` method is a wrapper for these
  private methods.
docstr: Args:
  
      inputs (tuple): `inputs` is the test batch and is a tuple of
              any, where the last element is assumed to be the labels for the
              batch. That is, `model(*batch[0:-1])` produces the output for
              `model`, and `batch[-1]` are the labels, if any. This is the same
              assumption made for each batch yielded by training dataset
              `train_dataset` - please see its documentation in `__init__` for
              more details on the assumed structure of a batch.
      k (int, optional): The number of proponents or opponents to return per test
              example.
              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 `train_dataset`, 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
  
  Returns:
      (indices, influence_scores) (namedtuple): `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 `train_dataset` 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 `train_dataset`
              on example `i` in the test batch represented by `inputs`.
docstr_tok: ["Args",":","inputs","(","tuple",")",":","`","inputs","`","is","the","test","batch","and","is","a","tuple","of","any",",","where","the","last","element","is","assumed","to","be","the","labels","for","the","batch",".","That","is",",","`","model","(","*","batch","[","0",":","-1","]",")","`","produces","the","output","for","`","model","`",",","and","`","batch","[","-1","]","`","are","the","labels",",","if","any",".","This","is","the","same","assumption","made","for","each","batch","yielded","by","training","dataset","`","train_dataset","`","-","please","see","its","documentation","in","`","__init__","`","for","more","details","on","the","assumed","structure","of","a","batch",".","k","(","int",",","optional",")",":","The","number","of","proponents","or","opponents","to","return","per","test","example",".","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","`","train_dataset","`",",","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","Returns",":","(","indices",",","influence_scores",")","(","namedtuple",")",":","`","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","`","train_dataset","`","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","`","train_dataset","`","on","example","`","i","`","in","the","test","batch","represented","by","`","inputs","`","."]
code: def _get_k_most_influential(
      self,
      inputs: Union[Tuple[Any, ...], DataLoader],
      k: int = 5,
      proponents: bool = True,
      show_progress: bool = False,
  ) -> KMostInfluentialResults:
      r"""
      Args:
  
          inputs (tuple): `inputs` is the test batch and is a tuple of
                  any, where the last element is assumed to be the labels for the
                  batch. That is, `model(*batch[0:-1])` produces the output for
                  `model`, and `batch[-1]` are the labels, if any. This is the same
                  assumption made for each batch yielded by training dataset
                  `train_dataset` - please see its documentation in `__init__` for
                  more details on the assumed structure of a batch.
          k (int, optional): The number of proponents or opponents to return per test
                  example.
                  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 `train_dataset`, 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
  
      Returns:
          (indices, influence_scores) (namedtuple): `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 `train_dataset` 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 `train_dataset`
                  on example `i` in the test batch represented by `inputs`.
      """
      pass
  
imports: import glob
  import warnings
  from abc import abstractmethod
  from os.path import join
  from typing import Any, Callable, Iterator, List, Optional, Tuple, Type, Union
  import torch
  from captum._utils.av import AV
  from captum._utils.common import _parse_version
  from captum._utils.progress import NullProgress, progress
  from captum.influence._core.influence import DataInfluence
  from captum.influence._utils.common import _check_loss_fn, _compute_jacobian_sample_wise_grads_per_batch, _format_inputs_dataset, _get_k_most_influential_helper, _gradient_dot_product, _influence_route_to_helpers, _load_flexible_state_dict, _self_influence_by_batches_helper, _set_active_parameters, KMostInfluentialResults
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 3 for calling the TracInCPBase obj's underlying member method code with example usage: obj._get_k_most_influential(inputs, k, proponents, show_progress) without return types