code: def _self_influence_by_batches_helper(
      self_influence_batch_fn: Callable,
      instance_name: str,
      inputs_dataset: Union[Tuple[Any, ...], DataLoader],
      show_progress: bool = False,
  ) -> Tensor:
      """
      Computes self influence scores for the examples in `inputs_dataset`, which is
      either a single batch or a Pytorch `DataLoader` that yields batches. The self
      influence scores for a single batch are computed using the
      `self_influence_batch_fn` input. Note that if `inputs_dataset` is a single batch,
      this will call `model` on that single batch, where `model` is the model used to
      compute self influence scores by `self_influence_batch_fn`, and if `inputs_dataset`
      yields batches, this will call `model` on each batch that is yielded. Therefore,
      please ensure that for both cases, the batch(es) that `model` is called
      with are not too large, so that there will not be an out-of-memory error. This
      implementation performs an outer iteration over all batches that
      `inputs_dataset` represents, and an inner iteration over checkpoints. The pros
      of this implementation are that showing the progress of the computation is
      straightforward.
  
      Args:
          self_influence_batch_fn (Callable): This is the function that computes self
                  influence scores for a single batch.
          instance_name (str): This is the name of the implementation class that
                  `self_influence_batch_fn` is a method of. This is used for displaying
                  warning messages.
          batches (tuple or DataLoader): Either a single tuple of any, or a
                  `DataLoader`, where each batch yielded is a tuple of any. In
                  either case, the tuple represents a single batch, 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 documentation for the
                  `train_dataset` argument to `TracInCP.__init__` for
                  more details on the assumed structure of a batch.
          show_progress (bool, optional): Computation of self influence scores can
                  take a long time if `inputs_dataset` represents many examples. If
                  `show_progress`is true, the progress of this computation will be
                  displayed. In particular, the number of batches for which self
                  influence scores have been computed 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:
          self_influence_scores (Tensor): This is a 1D tensor containing the self
                  influence scores of all examples in `inputs_dataset`, regardless of
                  whether it represents a single batch or a `DataLoader` that yields
                  batches.
      """
      # If `inputs_dataset` is not a `DataLoader`, turn it into one.
      inputs_dataset = _format_inputs_dataset(inputs_dataset)
  
      # If `show_progress` is true, create a progress bar that keeps track of how
      # many batches have been processed
      if show_progress:
          # First, try to determine length of progress bar if possible, with a
          # default of `None`
          inputs_dataset_len = None
          try:
              inputs_dataset_len = len(inputs_dataset)
          except TypeError:
              warnings.warn(
                  "Unable to determine the number of batches in `inputs_dataset`. "
                  "Therefore, if showing the progress of the computation of self "
                  "influence scores, only the number of batches processed can be "
                  "displayed, and not the percentage completion of the computation, "
                  "nor any time estimates."
              )
          # then create the progress bar
          inputs_dataset = progress(
              inputs_dataset,
              desc=f"Using {instance_name} to compute self influence. Processing batch",
              total=inputs_dataset_len,
          )
  
      # To compute self influence scores for each batch, we use
      # `_self_influence_by_checkpoints`, which can accept a tuple representing a
      # single batch as the `inputs_dataset` argument (as well as a DataLoader).
      # Because we are already displaying progress in terms of number of batches
      # processed in this method, we will not show progress for the call to
      # `_self_influence_by_checkpoints`.
      return torch.cat(
          [
              self_influence_batch_fn(batch, show_progress=False)
              for batch in inputs_dataset
          ]
      )
  
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 7 for calling a global function with example usage: _self_influence_by_batches_helper(_influence_batch_fn, instance_name, inputs_dataset, show_progress) and returns: torch