code: def test_simple_input_conv_no_grad(self) -> None:
      net = BasicModel_ConvNet_One_Conv()
  
      # this way we deactivate require_grad. Some models explicitly
      # do that before interpreting the model.
      for param in net.parameters():
          param.requires_grad = False
  
      inp = torch.arange(16).view(1, 1, 4, 4).float()
      self._grad_cam_test_assert(
          net, net.conv1, inp, [[[[11.25, 13.5], [20.25, 22.5]]]]
      )
  
imports: import unittest
  from typing import Any, Tuple, Union
  import torch
  from captum._utils.typing import TensorLikeList
  from captum.attr._core.layer.grad_cam import LayerGradCam
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer
  from torch import Tensor
  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_simple_input_conv_no_grad() without return types