class_docstr: `InfluenceFunctionBase` is a base class for implementations which compute the
  influence score as defined in the paper "Understanding Black-box Predictions via
  Influence Functions" (https://arxiv.org/pdf/1703.04730.pdf). This "infinitesimal"
  influence score approximately answers the question if a given training example
  were infinitesimally down-weighted and the model re-trained to optimality, how much
  would the loss on a given test example change. Mathematically, the aforementioned
  influence score is given by :math`\nabla_\theta L(x)' H^{-1} \nabla_\theta L(z)`,
  where :math`\nabla_\theta L(x)` is the gradient of the loss, considering only
  training example :math`x` with respect to (a subset of) model parameters
  :math`\theta`, :math`\nabla_\theta L(z)` is the analogous quantity for a test
  example :math`z`, and :math`H` is the Hessian of the (subset of) model parameters
  at a given model checkpoint. "Subset of model parameters" refers to the parameters
  specified by the `layers` initialization argument; for computational purposes,
  we may only consider the gradients / Hessian involving parameters in a subset of
  the model's layers. This is a commonly-taken approach in the research literature.
  
  There can be multiple implementations of this class, because although the paper
  defines a particular "infinitesimal" kind of influence score, there can be multiple
  ways to compute it, each with different levels of accuracy / scalability.
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 dataset represented by `inputs`.
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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 dataset represented by `inputs`.
      """
      pass
  
imports: import functools
  from abc import abstractmethod
  from operator import add
  from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from captum._utils.gradient import _extract_parameters_from_layers
  from captum.influence._core.influence import DataInfluence
  from captum.influence._utils.common import _check_loss_fn, _compute_batch_loss_influence_function_base, _compute_jacobian_sample_wise_grads_per_batch, _dataset_fn, _flatten_params, _format_inputs_dataset, _functional_call, _get_k_most_influential_helper, _influence_batch_intermediate_quantities_influence_function, _influence_helper_intermediate_quantities_influence_function, _influence_route_to_helpers, _load_flexible_state_dict, _params_to_names, _progress_bar_constructor, _self_influence_helper_intermediate_quantities_influence_function, _set_active_parameters, _top_eigen, _unflatten_params_factory, KMostInfluentialResults
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling the InfluenceFunctionBase obj's underlying member method code with example usage: obj._get_k_most_influential(inputs, k, proponents, show_progress) without return types