class_docstr: Evenly splits the video into clips of size clip_duration.
  If truncation_duration is set, clips sampled from [0, truncation_duration].
  If truncation_duration is not set, defaults to UniformClipSampler.
class_docstr_tok: ["Evenly","splits","the","video","into","clips","of","size","clip_duration",".","If","truncation_duration","is","set",",","clips","sampled","from","[","0",",","truncation_duration","]",".","If","truncation_duration","is","not","set",",","defaults","to","UniformClipSampler","."]
code: def __init__(
      self,
      clip_duration: Union[float, Fraction],
      stride: Optional[Union[float, Fraction]] = None,
      backpad_last: bool = False,
      eps: float = 1e-6,
      truncation_duration: float = None,
  ) -> None:
      super().__init__(clip_duration, stride, backpad_last, eps)
      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 UniformClipSamplerTruncateFromStart object from inherited base classes: UniformClipSampler with example: obj = UniformClipSamplerTruncateFromStart(clip_duration, stride, backpad_last, eps, truncation_duration)