code: def uniform_temporal_subsample_repeated(
      frames: torch.Tensor,
      frame_ratios: Tuple[int],
      temporal_dim: int = -3,
  ) -> Tuple[torch.Tensor]:
      """
      Prepare output as a list of tensors subsampled from the input frames. Each tensor
          maintain a unique copy of subsampled frames, which corresponds to a unique
          pathway.
  
      Args:
          frames (tensor): frames of images sampled from the video. Expected to have
              torch tensor (including int, long, float, complex, etc) with dimension
              larger than one.
          frame_ratios (tuple): ratio to perform temporal down-sampling for each pathways.
          temporal_dim (int): dimension of temporal.
  
      Returns:
          frame_list (tuple): list of tensors as output.
      """
      temporal_length = frames.shape[temporal_dim]
      frame_list = []
      for ratio in frame_ratios:
          pathway = uniform_temporal_subsample(
              frames, temporal_length // ratio, temporal_dim
          )
          frame_list.append(pathway)
  
      return frame_list
  
imports: import copy
  import math
  from typing import Tuple
  import numpy
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: uniform_temporal_subsample_repeated(frames, frame_ratios, temporal_dim) and returns: frame_list