class_docstr: Normalizes time series data.
  
  Parameters
  ----------
  normalize_algorithm : `str` or None, default None
      Which algorithm to use. Valid options are:
  
          - "MinMaxScaler" : `sklearn.preprocessing.MinMaxScaler`,
          - "MaxAbsScaler" : `sklearn.preprocessing.MaxAbsScaler`,
          - "StandardScaler" : `sklearn.preprocessing.StandardScaler`,
          - "RobustScaler" : `sklearn.preprocessing.RobustScaler`,
          - "Normalizer" : `sklearn.preprocessing.Normalizer`,
          - "QuantileTransformer" : `sklearn.preprocessing.QuantileTransformer`,
          - "PowerTransformer" : `sklearn.preprocessing.PowerTransformer`,
  
      If None, this transformer is a no-op. No normalization is done.
  
  normalize_params : `dict` or None, default None
      Params to initialize the normalization scaler/transformer.
  
  Attributes
  ----------
  scaler : `class`
      sklearn class used for normalization
  _is_fitted : `bool`
      Whether the transformer is fitted.
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code: def __init__(self, normalize_algorithm=None, normalize_params=None):
      # sets params without modification to ensure get_params() works in grid search
      self.normalize_algorithm = normalize_algorithm
      self.normalize_params = normalize_params
  
      self.scaler = None
      self._is_fitted = None
  
file_path: greykite/greykite/sklearn/transform/normalize_transformer.py
imports: import pandas
  from sklearn.base import BaseEstimator
  from sklearn.base import TransformerMixin
  from sklearn.exceptions import NotFittedError
  from sklearn.preprocessing import MaxAbsScaler
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import Normalizer
  from sklearn.preprocessing import PowerTransformer
  from sklearn.preprocessing import QuantileTransformer
  from sklearn.preprocessing import RobustScaler
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 1 to create a new NormalizeTransformer object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = NormalizeTransformer(normalize_algorithm, normalize_params)