code: def _get_multiargs_basic_config() -> (
      Tuple[Module, Tuple[Tensor, ...], Tuple[Tensor, ...], Any]
  ):
      model = BasicModel5_MultiArgs()
      additional_forward_args = ([2, 3], 1)
      inputs = (
          torch.tensor(
              [[1.5, 2.0, 34.3], [3.4, 1.2, 2.0]], requires_grad=True
          ),
          torch.tensor(
              [[3.0, 3.5, 23.2], [2.3, 1.2, 0.3]], requires_grad=True
          ),
      )
      grads = compute_gradients(
          model, inputs, additional_forward_args=additional_forward_args
      )
      return model, inputs, grads, additional_forward_args
  
imports: from typing import Any, cast, Tuple, Union
  import torch
  from captum._utils.gradient import compute_gradients
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._core.saliency import Saliency
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel5_MultiArgs
  from tests.helpers.classification_models import SoftmaxModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling a global function with example usage: _get_multiargs_basic_config() and returns: model,  inputs,  grads,  additional_forward_args