code: def r2plus1d_r50(
      pretrained: bool = False, progress: bool = True, **kwargs: Any
  ) -> nn.Module:
      r"""
  
      R(2+1)D model architecture from [1] with pretrained weights based on 16x4 setting
      on the Kinetics dataset. Model with pretrained weights has top1 accuracy of 76.01.
      (trained on 8*8 GPUs)
  
      [1] "A closer look at spatiotemporal convolutions for action recognition"
          Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, Manohar Paluri. CVPR 2018.
          https://arxiv.org/abs/1711.11248
  
      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.
      """
      model = create_r2plus1d(dropout_rate=0.5, **kwargs)
  
      if pretrained:
          path = checkpoint_paths["r2plus1d_r50"]
          # All models are loaded onto CPU by default
          checkpoint = load_state_dict_from_url(
              path, progress=progress, map_location="cpu"
          )
          state_dict = checkpoint["model_state"]
          model.load_state_dict(state_dict)
  
      return model
  
imports: from typing import Any
  import torch.nn
  from pytorchvideo.models.r2plus1d import create_r2plus1d
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 1 for calling a global function with example usage: r2plus1d_r50(pretrained, progress) and returns: model