code: def test_videoresenet_bottleneck_shapes():
      # test VideoResNet with Bottleneck
      resnet_bottleneck = VideoResNet(
          block=Bottleneck,
          conv_makers=[Conv3DSimple] * 4,
          layers=[2, 2, 2, 2],
          stem=BasicStem,
      )
      resnet_bottleneck.eval()
      output_batch = resnet_bottleneck(INPUT_BATCH_RGB)
      assert output_batch.size() == (2, 2048)
  
file_path: pykale/tests/embed/test_video_i3d_r3d.py
imports: import pytest
  import torch
  from kale.embed.video_i3d import InceptionI3d
  from kale.embed.video_res3d import BasicBlock, BasicStem, Bottleneck, Conv3DSimple, VideoResNet
  from kale.embed.video_se_i3d import SEInceptionI3DFlow, SEInceptionI3DRGB
  from kale.embed.video_se_res3d import se_r3d_18_flow, se_r3d_18_rgb
  from kale.utils.seed import set_seed
label_desc: Use image node_id 3 for calling a global function with example usage: test_videoresenet_bottleneck_shapes() without return types