class_docstr: ``nn.Module`` wrapper for ``pytorchvideo.transforms.functional.uniform_temporal_subsample``.
class_docstr_tok: ["``","nn.Module","``","wrapper","for","``","pytorchvideo.transforms.functional.uniform_temporal_subsample","``","."]
docstr: Args:
      num_samples (int): The number of equispaced samples to be selected
      temporal_dim (int): dimension of temporal to perform temporal subsample.
docstr_tok: ["Args",":","num_samples","(","int",")",":","The","number","of","equispaced","samples","to","be","selected","temporal_dim","(","int",")",":","dimension","of","temporal","to","perform","temporal","subsample","."]
code: def __init__(self, num_samples: int, temporal_dim: int = -3):
      """
      Args:
          num_samples (int): The number of equispaced samples to be selected
          temporal_dim (int): dimension of temporal to perform temporal subsample.
      """
      super().__init__()
      self._num_samples = num_samples
      self._temporal_dim = 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 1 to create a new UniformTemporalSubsample object from inherited base classes: torch.nn with example: obj = UniformTemporalSubsample(num_samples, temporal_dim)