code: def test_load_hubconf_detection(self):
      path = os.path.join(
          os.path.dirname(os.path.realpath(__file__)),
          "..",
      )
      input_clip_length = 32
      input_crop_size = 224
      input_channel = 3
      model = torch.hub.load(
          repo_or_dir=path,
          source="local",
          model="slowfast_r50_detection",
          pretrained=False,
      )
      self.assertIsNotNone(model)
  
      # Test forwarding.
      bbox_test_imputs = torch.tensor(
          [[0.0, 10, 15, 20, 25], [0.0, 11, 16, 21, 26]]
      )
      for tensor in TestSlowFast._get_inputs(
          input_channel, input_clip_length, input_crop_size
      ):
          with torch.no_grad():
              if tensor[0].shape[1] != input_channel:
                  with self.assertRaises(RuntimeError):
                      model(tensor, bbox_test_imputs)
                  continue
              model(tensor, bbox_test_imputs)
  
file_path: pytorchvideo/tests/test_models_slowfast.py
imports: import itertools
  import os
  import unittest
  from typing import Tuple
  import torch
  from pytorchvideo.models.slowfast import create_slowfast, create_slowfast_with_roi_head
  from pytorchvideo.transforms.functional import uniform_temporal_subsample_repeated
  from torch import nn
label_desc: Use image node_id 3 for calling the TestSlowFast obj's underlying member method code with example usage: obj.test_load_hubconf_detection() without return types