class_docstr: Configuration class for `ZMS` anomaly detection model. The transform of this config is actually a
  pre-processing step, followed by the desired number of lag transforms, and a final mean/variance
  normalization step. This full transform may be accessed as `ZMSConfig.full_transform`. Note that
  the normalization is inherited from `NormalizingConfig`.
docstr: Returns the full transform, including the pre-processing step, lags, and
  final mean/variance normalization.
docstr_tok: ["Returns","the","full","transform",",","including","the","pre-processing","step",",","lags",",","and","final","mean\/variance","normalization","."]
code: def full_transform(self):
      """
      Returns the full transform, including the pre-processing step, lags, and
      final mean/variance normalization.
      """
      return TransformSequence(
          [self.transform, self.lags, self.normalize]
      )
  
imports: from math import log
  from typing import List
  import numpy
  import pandas
  from merlion.models.base import NormalizingConfig
  from merlion.models.anomaly.base import DetectorBase, DetectorConfig
  from merlion.transform.base import Identity
  from merlion.transform.moving_average import LagTransform
  from merlion.transform.normalize import MeanVarNormalize
  from merlion.transform.sequence import TransformSequence, TransformStack
  from merlion.transform.resample import TemporalResample
  from merlion.utils import TimeSeries, UnivariateTimeSeries
label_desc: Use image node_id 2 for calling the ZMSConfig obj's underlying member method code with example usage: obj.full_transform() and returns: TransformSequence