code: def test_conv2d_stack(
      img_height: int,
      img_width: int,
      layers: Optional[List[Dict]],
      num_layers: Optional[int],
      first_in_channels: Optional[int],
  ) -> None:
      conv2d_stack = Conv2DStack(
          img_height=img_height,
          img_width=img_width,
          layers=layers,
          num_layers=num_layers,
          first_in_channels=first_in_channels,
      )
      input_tensor = torch.rand(2, 3, img_height, img_width)
      output_tensor = conv2d_stack(input_tensor)
      assert output_tensor.shape[1:] == conv2d_stack.output_shape
  
file_path: ludwig/tests/ludwig/modules/test_convolutional_modules.py
imports: from typing import Callable, Dict, List, Optional, Tuple, Union
  import pytest
  import torch
  from ludwig.modules.convolutional_modules import Conv1DLayer, Conv1DStack, Conv2DLayer, Conv2DLayerFixedPadding, Conv2DStack, ParallelConv1D, ParallelConv1DStack, ResNet, ResNetBlock, ResNetBlockLayer, ResNetBottleneckBlock
  from ludwig.utils.image_utils import get_img_output_shape
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
label_desc: Use image node_id 7 for calling a global function with example usage: test_conv2d_stack(img_height, img_width, layers, num_layers, first_in_channels) without return types