class_docstr: In Appendix F, Page 14 of the TracIn paper, they show that the calculation
  of the influence score of between a test example x' and a training example x,
  can be computed much more quickly than naive back-propagation in the special
  case when considering only gradients in the last fully-connected layer. This class
  computes influence scores for that special case. Note that the computed
  influence scores are exactly the same as when naive back-propagation is used -
  there is no loss in accuracy.
  
  In more detail regarding the influence score computation: let :math`x`
  and :math`\nabla_y f(y)` be the input and output-gradient of the last
  fully-connected layer, respectively, for a training example. Similarly, let
  :math`x'` and :math`\nabla_{y'} f(y')` be the corresponding quantities for
  a test example. Then, the influence score of the training example on the test
  example is the sum of the contribution from each checkpoint. The contribution from
  a given checkpoint is :math`(x^T x')(\nabla_y f(y)^T \nabla_{y'} f(y'))`.
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docstr: This is the key method of this class, and can be run in 2 different modes,
  where the mode that is run depends on the arguments passed to this method:
  
  - influence score mode: This mode is used if `k` is None. This mode computes
    the influence score of every example in training dataset `train_dataset`
    on every example in the test batch represented by `inputs`.
  - k-most influential mode: This mode is used if `k` is not None, and an int.
    This mode computes the proponents or opponents of every example in the
    test batch represented by `inputs`. In particular, for each test example in
    the test batch, this mode computes its proponents (resp. opponents),
    which are the indices in the training dataset `train_dataset` of the
    training examples with the `k` highest (resp. lowest) influence scores on the
    test example. Proponents are computed if `proponents` is True. Otherwise,
    opponents are computed. For each test example, this method also returns the
    actual influence score of each proponent (resp. opponent) on the test
    example.
  
  Args:
  
      inputs (tuple or DataLoader): `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): If not provided or `None`, the influence score mode will
              be run. Otherwise, the k-most influential mode will be run,
              and `k` is the number of proponents / opponents to return per
              example in the test batch.
              Default: None
      proponents (bool, optional): Whether seeking proponents (`proponents=True`)
              or opponents (`proponents=False`), if running in k-most influential
              mode.
              Default: True
      show_progress (bool, optional): For all modes, computation of results
              requires "training dataset computations": computations for each
              batch in the training dataset `train_dataset`, which may
              take a long time. If `show_progress` is true, the progress of
              "training dataset computations" will be displayed. In particular,
              the number of batches for which computations have 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:
      The return value of this method depends on which mode is run.
  
      - influence score mode: if this mode is run (`k` is None), returns a 2D
        tensor `influence_scores` of shape `(input_size, train_dataset_size)`,
        where `input_size` is the number of examples in the test batch, and
        `train_dataset_size` is the number of examples in training dataset
        `train_dataset`. In other words, `influence_scores[i][j]` is the
        influence score of the `j`-th example in `train_dataset` on the `i`-th
        example in the test batch.
      - k-most influential mode: if this mode is run (`k` is an int), returns
        a namedtuple `(indices, influence_scores)`. `indices` is a 2D tensor of
        shape `(input_size, k)`, where `input_size` is the number of examples in
        the test batch. If computing proponents (resp. opponents),
        `indices[i][j]` is the index in training dataset `train_dataset` of the
        example with the `j`-th highest (resp. lowest) influence score (out of
        the examples in `train_dataset`) on the `i`-th example in the test
        batch. `influence_scores` contains the corresponding influence scores.
        In particular, `influence_scores[i][j]` is the influence score of example
        `indices[i][j]` in `train_dataset` on example `i` in the test batch
        represented by `inputs`.
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code: def influence(  # type: ignore[override]
      self,
      inputs: Tuple[Any, ...],
      k: Optional[int] = None,
      proponents: bool = True,
      show_progress: bool = False,
  ) -> Union[Tensor, KMostInfluentialResults]:
      r"""
      This is the key method of this class, and can be run in 2 different modes,
      where the mode that is run depends on the arguments passed to this method:
  
      - influence score mode: This mode is used if `k` is None. This mode computes
        the influence score of every example in training dataset `train_dataset`
        on every example in the test batch represented by `inputs`.
      - k-most influential mode: This mode is used if `k` is not None, and an int.
        This mode computes the proponents or opponents of every example in the
        test batch represented by `inputs`. In particular, for each test example in
        the test batch, this mode computes its proponents (resp. opponents),
        which are the indices in the training dataset `train_dataset` of the
        training examples with the `k` highest (resp. lowest) influence scores on the
        test example. Proponents are computed if `proponents` is True. Otherwise,
        opponents are computed. For each test example, this method also returns the
        actual influence score of each proponent (resp. opponent) on the test
        example.
  
      Args:
  
          inputs (tuple or DataLoader): `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): If not provided or `None`, the influence score mode will
                  be run. Otherwise, the k-most influential mode will be run,
                  and `k` is the number of proponents / opponents to return per
                  example in the test batch.
                  Default: None
          proponents (bool, optional): Whether seeking proponents (`proponents=True`)
                  or opponents (`proponents=False`), if running in k-most influential
                  mode.
                  Default: True
          show_progress (bool, optional): For all modes, computation of results
                  requires "training dataset computations": computations for each
                  batch in the training dataset `train_dataset`, which may
                  take a long time. If `show_progress` is true, the progress of
                  "training dataset computations" will be displayed. In particular,
                  the number of batches for which computations have 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:
          The return value of this method depends on which mode is run.
  
          - influence score mode: if this mode is run (`k` is None), returns a 2D
            tensor `influence_scores` of shape `(input_size, train_dataset_size)`,
            where `input_size` is the number of examples in the test batch, and
            `train_dataset_size` is the number of examples in training dataset
            `train_dataset`. In other words, `influence_scores[i][j]` is the
            influence score of the `j`-th example in `train_dataset` on the `i`-th
            example in the test batch.
          - k-most influential mode: if this mode is run (`k` is an int), returns
            a namedtuple `(indices, influence_scores)`. `indices` is a 2D tensor of
            shape `(input_size, k)`, where `input_size` is the number of examples in
            the test batch. If computing proponents (resp. opponents),
            `indices[i][j]` is the index in training dataset `train_dataset` of the
            example with the `j`-th highest (resp. lowest) influence score (out of
            the examples in `train_dataset`) on the `i`-th example in the test
            batch. `influence_scores` contains the corresponding influence scores.
            In particular, `influence_scores[i][j]` is the influence score of example
            `indices[i][j]` in `train_dataset` on example `i` in the test batch
            represented by `inputs`.
      """
      assert inputs is not None, (
          "`inputs` argument is required."
          "If you wish to calculate self influence scores,"
          " please use the `self_influence` method instead."
      )
      return _influence_route_to_helpers(
          self,
          inputs,
          k,
          proponents,
          show_progress=show_progress,
      )
  
imports: import threading
  import warnings
  from collections import defaultdict
  from typing import Any, Callable, cast, Dict, Iterator, List, Optional, Tuple, Union
  import torch
  from captum._utils.common import _get_module_from_name, _sort_key_list
  from captum._utils.gradient import _gather_distributed_tensors
  from captum._utils.progress import NullProgress, progress
  from captum.influence._core.tracincp import _influence_route_to_helpers, KMostInfluentialResults, TracInCPBase
  from captum.influence._utils.common import _check_loss_fn, _format_inputs_dataset, _get_k_most_influential_helper, _jacobian_loss_wrt_inputs, _load_flexible_state_dict, _self_influence_by_batches_helper, _tensor_batch_dot
  from captum.influence._utils.nearest_neighbors import AnnoyNearestNeighbors, NearestNeighbors
  from captum.log import log_usage
  from torch import device, Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 2 for calling the TracInCPFast obj's underlying member method code with example usage: obj.influence(inputs, k, proponents, show_progress) and returns: _influence_route_to_helpers