code: def slow_r50(
      pretrained: bool = False, progress: bool = True, **kwargs: Any
  ) -> nn.Module:
      r"""
      Slow R50 model architecture [1] with pretrained weights based on 8x8 setting
      on the Kinetics dataset. Model with pretrained weights has top1 accuracy of 74.58.
  
      [1] "SlowFast Networks for Video Recognition"
          Christoph Feichtenhofer et al
          https://arxiv.org/pdf/1812.03982.pdf
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on the Kinetics dataset
          progress (bool): If True, displays a progress bar of the download to stderr
          kwargs: use these to modify any of the other model settings. All the
              options are defined in pytorchvideo/models/resnet.py
  
      NOTE: to use the pretrained model, do not modify the model configuration
      via the kwargs. Only modify settings via kwargs to initialize a new model
      without pretrained weights.
      """
      return _resnet(
          pretrained=pretrained,
          progress=progress,
          checkpoint_path=checkpoint_paths["slow_r50"],
          stem_conv_kernel_size=(1, 7, 7),
          head_pool_kernel_size=(8, 7, 7),
          model_depth=50,
          **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 2 for calling a global function with example usage: slow_r50(pretrained, progress) and returns: _resnet