code: def test_minimal_pert_additional_forward_args(self) -> None:
      model = BasicModel_MultiLayer()
      text_inp = [["abc", "zyd", "ghi"], ["abc", "uyd", "ghi"]]
      additional_forward_args = torch.ones((2, 3)) * -97
  
      model = BasicModel_MultiLayer()
      minimal_pert = MinParamPerturbation(
          forward_func=model,
          attack=add_char_batch,
          arg_name="char_val",
          arg_min=0,
          arg_max=26,
          arg_step=1,
          preproc_fn=batch_text_preproc_fn,
          apply_before_preproc=True,
          correct_fn=alt_correct_fn,
      )
      expected_list = [["abc", "uzyd", "ghi"], ["abc", "uuyd", "ghi"]]
  
      target_inp, pert = minimal_pert.evaluate(
          text_inp,
          target=1,
          attack_kwargs={"ind": 1},
          correct_fn_kwargs={"threshold": 100},
          perturbations_per_eval=15,
          additional_forward_args=(additional_forward_args,),
      )
      self.assertEqual(pert, 5)
      self.assertListEqual(target_inp, expected_list)
  
      target_inp_single, pert_single = minimal_pert.evaluate(
          text_inp,
          target=1,
          attack_kwargs={"ind": 1},
          correct_fn_kwargs={"threshold": 100},
          additional_forward_args=(additional_forward_args,),
      )
      self.assertEqual(pert_single, 5)
      self.assertListEqual(target_inp_single, expected_list)
  
file_path: captum/tests/robust/test_min_param_perturbation.py
imports: from typing import cast, List
  import torch
  from captum.robust import MinParamPerturbation
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel_MultiLayer
  from torch import Tensor
label_desc: Use image node_id 5 for calling the Test obj's underlying member method code with example usage: obj.test_minimal_pert_additional_forward_args() without return types