code: def test_action_recognition_slowfast_models():
      use_cuda = True
      if torch.cuda.device_count() == 0:
          use_cuda = False
  
      models = [
          "slowfast_4x16_resnet50_kinetics400",
          "slowfast_4x16_resnet101_kinetics400",
      ]
      x = torch.rand(2, 3, 32, 224, 224)
      _test_model_list(
          models, use_cuda, x, pretrained=False, num_classes=400
      )
  
      models = ["slowfast_4x16_resnet50_kinetics400"]
      _test_model_list(
          models, use_cuda, x, pretrained=True, num_classes=400
      )
  
      models = [
          "slowfast_8x8_resnet50_kinetics400",
          "slowfast_8x8_resnet101_kinetics400",
      ]
      x = torch.rand(2, 3, 32, 224, 224)
      _test_model_list(
          models, use_cuda, x, pretrained=False, num_classes=400
      )
      _test_model_list(
          models, use_cuda, x, pretrained=True, num_classes=400
      )
  
      models = [
          "slowfast_16x8_resnet101_kinetics400",
          "slowfast_16x8_resnet101_50_50_kinetics400",
      ]
      x = torch.rand(2, 3, 64, 224, 224)
      _test_model_list(
          models, use_cuda, x, pretrained=False, num_classes=400
      )
  
      models = ["slowfast_16x8_resnet50_sthsthv2"]
      x = torch.rand(2, 3, 64, 224, 224)
      _test_model_list(
          models, use_cuda, x, pretrained=False, num_classes=174
      )
      _test_model_list(
          models, use_cuda, x, pretrained=True, num_classes=174
      )
  
file_path: gluon-cv/tests/model_zoo_torch/test_model_zoo_torch.py
imports: from __future__ import print_function
  import warnings
  import logging
  import torch
  import torch.nn
  from gluoncv.torch.model_zoo import get_model, get_model_list
  from gluoncv.torch.engine.config import get_cfg_defaults
label_desc: Use image node_id 6 for calling a global function with example usage: test_action_recognition_slowfast_models() without return types