class_docstr: ``nn.Module`` wrapper for
  ``pytorchvideo.transforms.functional.uniform_temporal_subsample_repeated``.
class_docstr_tok: ["``","nn.Module","``","wrapper","for","``","pytorchvideo.transforms.functional.uniform_temporal_subsample_repeated","``","."]
docstr: Args:
      x (torch.Tensor): video tensor with shape (C, T, H, W).
docstr_tok: ["Args",":","x","(","torch.Tensor",")",":","video","tensor","with","shape","(","C",",","T",",","H",",","W",")","."]
code: def forward(self, x: torch.Tensor):
      """
      Args:
          x (torch.Tensor): video tensor with shape (C, T, H, W).
      """
      return pytorchvideo.transforms.functional.uniform_temporal_subsample_repeated(
          x, self._frame_ratios, self._temporal_dim
      )
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import pytorchvideo.transforms.functional
  import torch
  import torchvision.transforms
label_desc: Use image node_id 2 for calling the UniformTemporalSubsampleRepeated obj's underlying member method code with example usage: obj.forward(x) and returns: pytorchvideo