class_docstr: Transform for converting a video clip into a list of 2 clips with
  different temporal granualirity as needed by the SlowFast video
  model.
  
  For more details, refere to the paper,
  Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He.
  "SlowFast networks for video recognition."
  https://arxiv.org/pdf/1812.03982.pdf
  
  Args:
      alpha (int): Number of frames to sub-sample from the given clip
      to create the second clip.
class_docstr_tok: ["Transform","for","converting","a","video","clip","into","a","list","of","2","clips","with","different","temporal","granualirity","as","needed","by","the","SlowFast","video","model",".","For","more","details",",","refere","to","the","paper",",","Christoph","Feichtenhofer",",","Haoqi","Fan",",","Jitendra","Malik",",","and","Kaiming","He",".","``","SlowFast","networks","for","video","recognition",".","''","https",":","\/\/arxiv.org\/pdf\/1812.03982.pdf","Args",":","alpha","(","int",")",":","Number","of","frames","to","sub-sample","from","the","given","clip","to","create","the","second","clip","."]
docstr: Args:
      frames (tensor): frames of images sampled from the video. The
      dimension is `channel` x `num frames` x `height` x `width`.
  Returns:
      frame_list (list): list of tensors with the dimension of
      `channel` x `num frames` x `height` x `width`.
docstr_tok: ["Args",":","frames","(","tensor",")",":","frames","of","images","sampled","from","the","video",".","The","dimension","is","`","channel","`","x","`","num","frames","`","x","`","height","`","x","`","width","`",".","Returns",":","frame_list","(","list",")",":","list","of","tensors","with","the","dimension","of","`","channel","`","x","`","num","frames","`","x","`","height","`","x","`","width","`","."]
code: def __call__(self, frames: torch.Tensor) -> List[torch.Tensor]:
      """
      Args:
          frames (tensor): frames of images sampled from the video. The
          dimension is `channel` x `num frames` x `height` x `width`.
      Returns:
          frame_list (list): list of tensors with the dimension of
          `channel` x `num frames` x `height` x `width`.
      """
      fast_pathway = frames
      # Perform temporal sampling from the fast pathway.
      slow_pathway = torch.index_select(
          frames,
          1,
          torch.linspace(
              0, frames.shape[1] - 1, frames.shape[1] // self.alpha
          ).long(),
      )
      frame_list = [slow_pathway, fast_pathway]
      return frame_list
  
imports: import random
  from typing import Any, Callable, Dict, Iterable, List, Mapping, Sequence
  import hydra
  import torch
  import torchvision
  from PIL import Image, ImageFilter
  from pytorchvideo.transforms import MixVideo
  from torchvision.transforms import Compose
label_desc: Use image node_id 2 for calling the SlowFastPackPathway obj's underlying member method code with example usage: obj.__call__(frames) and returns: frame_list