class_docstr: Transform for converting video frames as a list of tensors.
  
  Args:
      alpha (`int`):
          alpha value
  
  Returns:
      a list of tensors [slow_pathway, fast_pathway]
class_docstr_tok: ["Transform","for","converting","video","frames","as","a","list","of","tensors",".","Args",":","alpha","(","`","int","`",")",":","alpha","value","Returns",":","a","list","of","tensors","[","slow_pathway",",","fast_pathway","]"]
code: def forward(self, frames: torch.Tensor):
      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 os
  import logging
  import numpy
  import numbers
  import torch
  from torch import nn
  from torchvision.transforms import Compose
label_desc: Use image node_id 2 for calling the PackPathway obj's underlying member method code with example usage: obj.forward(frames) and returns: frame_list