code: def test_tracin_self_influence_dataloader_vs_single_batch(
      self,
      reduction: str,
      tracin_constructor: Callable,
      unpack_inputs: bool,
  ) -> None:
      # tests that the result of calling the public method `self_influence` for a
      # DataLoader of batches is the same as when the batches are collated into a
      # single batch
      with tempfile.TemporaryDirectory() as tmpdir:
          (
              net,
              train_dataset,
          ) = get_random_model_and_data(
              tmpdir, unpack_inputs, return_test_data=False
          )
  
          # create a single batch representing the entire dataset
          single_batch = next(
              iter(
                  DataLoader(
                      train_dataset, batch_size=len(train_dataset)
                  )
              )
          )
  
          # create a dataloader that yields batches from the dataset
          dataloader = DataLoader(train_dataset, batch_size=5)
  
          # create tracin instance
          criterion = nn.MSELoss(reduction=reduction)
          batch_size = 5
          tracin = tracin_constructor(
              net,
              train_dataset,
              tmpdir,
              batch_size,
              criterion,
          )
  
          # compute self influence using `self_influence` when passing in a single
          # batch
          single_batch_self_influence = tracin.self_influence(
              single_batch
          )
  
          # compute self influence using `self_influence` when passing in a
          # dataloader with the same examples
          dataloader_self_influence = tracin.self_influence(dataloader)
  
          # the two self influences should be equal
          assertTensorAlmostEqual(
              self,
              single_batch_self_influence,
              dataloader_self_influence,
              delta=0.01,  # due to numerical issues, we can't set this to 0.0
              mode="max",
          )
  
file_path: captum/tests/influence/_core/test_tracin_self_influence.py
imports: import tempfile
  from typing import Callable, Union
  import torch
  import torch.nn
  from captum.influence._core.arnoldi_influence_function import ArnoldiInfluenceFunction
  from captum.influence._core.influence_function import NaiveInfluenceFunction
  from captum.influence._core.tracincp import TracInCP, TracInCPBase
  from captum.influence._core.tracincp_fast_rand_proj import TracInCPFast
  from parameterized import parameterized
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.influence._utils.common import _format_batch_into_tuple, build_test_name_func, DataInfluenceConstructor, get_random_model_and_data
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the TestTracInSelfInfluence obj's underlying member method code with example usage: obj.test_tracin_self_influence_dataloader_vs_single_batch(reduction, tracin_constructor, unpack_inputs) without return types