class_docstr: Applies the Box-Cox power transform to the time series, with power lmbda.
  When lmbda is None, we
  When lmbda > 0, it is ((x + offset) ** lmbda - 1) / lmbda.
  When lmbda == 0, it is ln(lmbda + offset).
code: def __init__(self, lmbda=None, offset=0.0):
      super().__init__()
      if lmbda is not None:
          if isinstance(lmbda, dict):
              assert all(
                  isinstance(x, (int, float)) for x in lmbda.values()
              )
          else:
              assert isinstance(lmbda, (int, float))
      self.lmbda = lmbda
      self.offset = offset
  
imports: from collections import OrderedDict
  import logging
  from typing import Iterable, Mapping
  import numpy
  import pandas
  import scipy.special
  import scipy.stats
  from sklearn.preprocessing import StandardScaler
  from merlion.transform.base import InvertibleTransformBase, TransformBase
  from merlion.utils import UnivariateTimeSeries, TimeSeries
label_desc: Use image node_id 1 to create a new BoxCoxTransform object from inherited base classes: InvertibleTransformBase with example: obj = BoxCoxTransform(lmbda, offset)