class_docstr: Uniformly subsample ``num_samples`` indices from the temporal dimension of the video.
  
  Videos are expected to be of shape ``[..., T, C, H, W]`` where ``T`` denotes the temporal dimension.
  
  When ``num_samples`` is larger than the size of temporal dimension of the video, it
  will sample frames based on nearest neighbor interpolation.
  
  Args:
      num_samples (int): The number of equispaced samples to be selected
code: def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
      return self._call_kernel(
          F.uniform_temporal_subsample, inpt, self.num_samples
      )
  
imports: from typing import Any, Dict
  import torch
  from torchvision.transforms.v2 import functional, Transform
label_desc: Use image node_id 2 for calling the UniformTemporalSubsample obj's underlying member method code with example usage: obj._transform(inpt, params) and returns: self