class_docstr: Randomly samples multiple clips of size clip_duration from the videos.
  If truncation_duration is set, clips sampled from [0, truncation_duration].
  If truncation_duration is not set, defaults to RandomMultiClipSampler.
class_docstr_tok: ["Randomly","samples","multiple","clips","of","size","clip_duration","from","the","videos",".","If","truncation_duration","is","set",",","clips","sampled","from","[","0",",","truncation_duration","]",".","If","truncation_duration","is","not","set",",","defaults","to","RandomMultiClipSampler","."]
code: def __init__(
      self,
      clip_duration: float,
      num_clips: int,
      truncation_duration: float = None,
  ) -> None:
      super().__init__(clip_duration, num_clips)
      self.truncation_duration = truncation_duration
  
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 to create a new RandomMultiClipSamplerTruncateFromStart object from inherited base classes: RandomMultiClipSampler with example: obj = RandomMultiClipSamplerTruncateFromStart(clip_duration, num_clips, truncation_duration)