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 __init__(self, num_samples: int):
      super().__init__()
      self.num_samples = num_samples
  
imports: from typing import Any, Dict
  import torch
  from torchvision.transforms.v2 import functional, Transform
label_desc: Use image node_id 1 to create a new UniformTemporalSubsample object from inherited base classes: Transform with example: obj = UniformTemporalSubsample(num_samples)