code: def make_clip_sampler(sampling_type: str, *args) -> ClipSampler:
      """
      Constructs the clip samplers found in ``pytorchvideo.data.clip_sampling`` from the
      given arguments.
  
      Args:
          sampling_type (str): choose clip sampler to return. It has three options:
  
              * uniform: constructs and return ``UniformClipSampler``
              * random: construct and return ``RandomClipSampler``
              * constant_clips_per_video: construct and return ``ConstantClipsPerVideoSampler``
  
          *args: the args to pass to the chosen clip sampler constructor.
      """
      if sampling_type == "uniform":
          return UniformClipSampler(*args)
      elif sampling_type == "random":
          return RandomClipSampler(*args)
      elif sampling_type == "constant_clips_per_video":
          return ConstantClipsPerVideoSampler(*args)
      elif sampling_type == "random_multi":
          return RandomMultiClipSampler(*args)
      else:
          raise NotImplementedError(f"{sampling_type} not supported")
  
imports: import random
  from abc import ABC, abstractmethod
  from fractions import Fraction
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
label_desc: Use image node_id 1 for calling a global function with example usage: make_clip_sampler(sampling_type) and returns: UniformClipSampler, RandomClipSampler, ConstantClipsPerVideoSampler, RandomMultiClipSampler