code: def _test_tracin_regression_setup(
      self, tmpdir: str, features: int, use_gpu: bool = False
  ):
      low = 1
      high = 17
      dataset = RangeDataset(low, high, features, use_gpu)
      net = CoefficientNet(in_features=features)
  
      checkpoint_name = "-".join(["checkpoint-reg", "0" + ".pt"])
      torch.save(
          net.state_dict(), os.path.join(tmpdir, checkpoint_name)
      )
  
      weights = [0.4379, 0.1653, 0.5132, 0.3651, 0.9992]
  
      for i, weight in enumerate(weights):
          net.fc1.weight.data.fill_(weight)
          net_adjusted = (
              _wrap_model_in_dataparallel(net) if use_gpu else net
          )
          checkpoint_name = "-".join(
              ["checkpoint-reg", str(i + 1) + ".pt"]
          )
          torch.save(
              net_adjusted.state_dict(),
              os.path.join(tmpdir, checkpoint_name),
          )
  
      return dataset, net_adjusted
  
file_path: captum/tests/influence/_core/test_tracin_regression.py
imports: import os
  import tempfile
  from typing import Callable, cast, Optional
  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
  from captum.influence._core.tracincp_fast_rand_proj import TracInCPFast, TracInCPFastRandProj
  from parameterized import parameterized
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.influence._utils.common import _isSorted, _wrap_model_in_dataparallel, build_test_name_func, CoefficientNet, DataInfluenceConstructor, IdentityDataset, RangeDataset
label_desc: Use image node_id 1 for calling the TestTracInRegression obj's underlying member method code with example usage: obj._test_tracin_regression_setup(tmpdir, features, use_gpu) and returns: dataset,  net_adjusted