code: def test_improper_dims_multi_input_conv(self) -> None:
      net = BasicModel_ConvNet_One_Conv()
      inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
      inp2 = torch.ones(1)
      ex = [
          [
              [
                  [14.5, 29.0, 38.0, 19.0],
                  [29.0, 58.0, 76.0, 38.0],
                  [65.0, 130.0, 148.0, 74.0],
                  [32.5, 65.0, 74.0, 37.0],
              ]
          ]
      ]
      self._guided_grad_cam_test_assert(
          net, net.conv1, (inp, inp2), (ex, [])
      )
  
file_path: captum/tests/attr/test_guided_grad_cam.py
imports: import unittest
  from typing import Any
  import torch
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.guided_grad_cam import GuidedGradCam
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv
  from torch.nn import Module
label_desc: Use image node_id 5 for calling the Test obj's underlying member method code with example usage: obj.test_improper_dims_multi_input_conv() without return types