code: def slow_r50_detection(
      pretrained: bool = False, progress: bool = True, **kwargs: Any
  ) -> nn.Module:
      r"""
      Slow R50 model architecture [1] with pretrained weights based on 4x16 setting.
      The model is initially trained on Kinetics dataset for classification and later
      finetuned on AVA dataset for detection.
  
      [1] Christoph Feichtenhofer et al, "SlowFast Networks for Video Recognition"
          https://arxiv.org/pdf/1812.03982.pdf
      """
      return _resnet(
          pretrained=pretrained,
          progress=progress,
          checkpoint_path=checkpoint_paths["slow_r50_detection"],
          model_builder=create_resnet_with_roi_head,
          **kwargs,
      )
  
file_path: pytorchvideo/pytorchvideo/models/hub/resnet.py
imports: from typing import Any, Callable
  import torch.nn
  from pytorchvideo.models.resnet import create_resnet, create_resnet_with_roi_head
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 3 for calling a global function with example usage: slow_r50_detection(pretrained, progress) and returns: _resnet