code: def test_conv2d_layer_fixed_padding(
      img_height: int,
      img_width: int,
      in_channels: int,
      stride: int,
      groups: int,
  ) -> None:
      conv2d_fixed_padding = Conv2DLayerFixedPadding(
          img_height=img_height,
          img_width=img_width,
          in_channels=in_channels,
          stride=stride,
          groups=groups,
      )
      input_tensor = torch.rand(2, in_channels, img_height, img_width)
      output_tensor = conv2d_fixed_padding(input_tensor)
      assert (
          output_tensor.shape[1:] == conv2d_fixed_padding.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 8 for calling a global function with example usage: test_conv2d_layer_fixed_padding(img_height, img_width, in_channels, stride, groups) without return types