docstr: computes the jacobians of all examples in `inputs_dataset`, for all
  checkpoints. if `aggregate` is True, the jacobians for examples are summed.
  returns a list where each element corresponds to a checkpoint. this logic is
  separated into a helper function because it is used by both `_influence` and
  `_get_k_most_influential`.
docstr_tok: ["computes","the","jacobians","of","all","examples","in","`","inputs_dataset","`",",","for","all","checkpoints",".","if","`","aggregate","`","is","True",",","the","jacobians","for","examples","are","summed",".","returns","a","list","where","each","element","corresponds","to","a","checkpoint",".","this","logic","is","separated","into","a","helper","function","because","it","is","used","by","both","`","_influence","`","and","`","_get_k_most_influential","`","."]
code: def _get_checkpoint_jacobians(
      self,
      inputs_dataset: DataLoader,
      aggregate: bool,
      loss_fn: Optional[Union[Module, Callable]] = None,
  ) -> List[Tuple[Tensor, ...]]:
      """
      computes the jacobians of all examples in `inputs_dataset`, for all
      checkpoints. if `aggregate` is True, the jacobians for examples are summed.
      returns a list where each element corresponds to a checkpoint. this logic is
      separated into a helper function because it is used by both `_influence` and
      `_get_k_most_influential`.
      """
      inputs_checkpoint_jacobians = []
      for checkpoint in self.checkpoints:
          self.checkpoints_load_func(self.model, checkpoint)
          if aggregate:
              inputs_checkpoint_jacobians.append(
                  self._sum_jacobians(
                      inputs_dataset, loss_fn, self.reduction_type
                  )
              )
          else:
              inputs_checkpoint_jacobians.append(
                  self._concat_jacobians(
                      inputs_dataset, loss_fn, self.reduction_type
                  )
              )
      return inputs_checkpoint_jacobians
  
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 7 for calling the TracInCP obj's underlying member method code with example usage: obj._get_checkpoint_jacobians(inputs_dataset, aggregate, loss_fn) and returns: inputs_checkpoint_jacobians