class_docstr: Injects random level shift anomalies into a time series.
  
  A level shift is a sudden change of level in a time series. It is equivalent to
  a shock that, when applied to ``y_t``, is also applied to every datapoint after ``t``.
code: def __init__(self, **kwargs):
      kwargs["persist_shock"] = True
      # We count a level shift anomaly as lasting for 20 points after the fact
      kwargs["anom_width_range"] = (20, 20)
      super().__init__(**kwargs)
  
imports: from abc import abstractmethod
  from typing import Tuple
  import numpy
  import pandas
  from merlion.transform.base import Identity, TransformBase
  from merlion.transform.bound import LowerUpperClip
  from merlion.utils.time_series import UnivariateTimeSeries, TimeSeries
label_desc: Use image node_id 1 to create a new LevelShift object from inherited base classes: Shock with example: obj = LevelShift()