code: def _basic_computation_tracincp_fast(
      influence_instance: TracInCPFast,
      inputs: Tuple[Any, ...],
      targets: Tensor,
      loss_fn: Optional[Union[Module, Callable]] = None,
      reduction_type: Optional[str] = None,
  ):
      """
      For instances of TracInCPFast and children classes, computation of influence scores
      or self influence scores repeatedly calls this function for different checkpoints
      and batches. These computations involve a loss function. If `test` is True, the
      loss function is `self.loss_fn`. If `test` is False, the loss function is
      `self.test_loss_fn`. These two attributes were set in initialization, with
      `self.loss_fn` equal to the `loss_fn` initialization argument, and
      `self.test_loss_fn` equal to the `test_loss_fn` initialization argument if it was
      provided, and `loss_fn` otherwise.
  
      Args:
  
          influence_instance (TracInCPFast): A instance of TracInCPFast or its children.
                  We assume `influence_instance` has a `loss_fn` attribute, i.e. the loss
                  function applied to the output of the last fully-connected layer, as
                  well as a `reduction_type` attribute, which indicates whether `loss_fn`
                  reduces the per-example losses by using their mean or sum. The
                  `reduction_type` attribute must either be "mean" or "sum".
          inputs (tuple[Any, ...]): A batch of examples, which could be a training batch
                  or test batch, depending which method is the caller. Does not
                  represent labels, which are passed as `targets`. The assumption is
                  that `model(*inputs)` produces the predictions for the batch.
          targets (Tensor): If computing influence scores on a loss function,
                  these are the labels corresponding to the batch `inputs`.
          loss_fn (Callable, optional): The loss function to use when computing the
                  jacobian.
          reduction_type (str, optional): The reduction type of `loss_fn`. This argument
                  is only used if `sample_wise_grads_per_batch` was true in
                  initialization of `influence_instance`.
  
      Returns:
          (input_jacobians, layer_inputs) (tuple): `input_jacobians` is a 2D tensor,
                  where each row is the jacobian of the loss, with respect to the
                  *output* of the last fully-connected layer. `layer_inputs` is a 1D
                  tensor, where each row is the *input* to the last fully-connected
                  layer. For both, the length is the number of examples in the batch
                  represented by `inputs` and `targets`.
      """
      layer_inputs: Dict[device, Tuple[Tensor, ...]] = defaultdict()
      lock = threading.Lock()
  
      def hook_wrapper(original_module):
          def _capture_inputs(layer, input, output) -> None:
              r"""Save activations into layer_inputs in forward pass"""
              with lock:
                  is_eval_tuple = isinstance(input, tuple)
                  if is_eval_tuple:
                      layer_inputs_val = tuple(
                          inp.detach() for inp in input
                      )
                  else:
                      layer_inputs_val = input.detach()
                  layer_inputs[
                      layer_inputs_val[0].device
                  ] = layer_inputs_val
  
          return _capture_inputs
  
      assert isinstance(influence_instance.final_fc_layer, Module)
      handle = influence_instance.final_fc_layer.register_forward_hook(
          hook_wrapper(influence_instance.final_fc_layer)
      )
      out = influence_instance.model(*inputs)
  
      assert loss_fn is not None, "loss function is required"
      assert reduction_type in [
          "sum",
          "mean",
      ], 'reduction_type must be either "mean" or "sum"'
      input_jacobians = _jacobian_loss_wrt_inputs(
          loss_fn,
          out,
          targets,
          influence_instance.vectorize,
          reduction_type,
      )
      handle.remove()
  
      device_ids = cast(
          Union[None, List[int]],
          influence_instance.model.device_ids
          if hasattr(influence_instance.model, "device_ids")
          else None,
      )
      key_list = _sort_key_list(list(layer_inputs.keys()), device_ids)
  
      _layer_inputs = _gather_distributed_tensors(
          layer_inputs, key_list=key_list
      )[0]
  
      assert len(input_jacobians.shape) == 2
  
      return input_jacobians, _layer_inputs
  
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 1 for calling a global function with example usage: _basic_computation_tracincp_fast(influence_instance, inputs, targets, loss_fn, reduction_type) and returns: input_jacobians,  _layer_inputs, _capture_inputs