code: def test_simple_input_conv_deconv(self) -> None:
      net = BasicModel_ConvNet_One_Conv()
      inp = 1.0 * torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
      exp = [
          [2.0, 3.0, 3.0, 1.0],
          [3.0, 5.0, 5.0, 2.0],
          [3.0, 5.0, 5.0, 2.0],
          [1.0, 2.0, 2.0, 1.0],
      ]
      exp = torch.tensor(exp).view(1, 1, 4, 4)
      self._deconv_test_assert(net, (inp,), (exp,))
  
imports: from __future__ import print_function
  import unittest
  from typing import Any, Tuple, Union
  import torch
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.guided_backprop_deconvnet import Deconvolution
  from captum.attr._core.neuron.neuron_guided_backprop_deconvnet import NeuronDeconvolution
  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 1 for calling the Test obj's underlying member method code with example usage: obj.test_simple_input_conv_deconv() without return types