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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docstr: Fits the normalization transform.
  
  Parameters
  ----------
  X : `pandas.DataFrame`
      Training input data. e.g. each column is a timeseries.
      Columns are expected to be numeric.
  y : None
      There is no need of a target in a transformer, yet the pipeline API
      requires this parameter.
  
  Returns
  -------
  self : object
      Returns self.
docstr_tok: ["Fits","the","normalization","transform",".","Parameters","--","--","--","--","--","X",":","`","pandas.DataFrame","`","Training","input","data",".","e.g",".","each","column","is","a","timeseries",".","Columns","are","expected","to","be","numeric",".","y",":","None","There","is","no","need","of","a","target","in","a","transformer",",","yet","the","pipeline","API","requires","this","parameter",".","Returns","--","--","--","-","self",":","object","Returns","self","."]
code: def fit(self, X, y=None):
      """Fits the normalization transform.
  
      Parameters
      ----------
      X : `pandas.DataFrame`
          Training input data. e.g. each column is a timeseries.
          Columns are expected to be numeric.
      y : None
          There is no need of a target in a transformer, yet the pipeline API
          requires this parameter.
  
      Returns
      -------
      self : object
          Returns self.
      """
      assert isinstance(X, pd.DataFrame)
      self._is_fitted = True
      if self.normalize_algorithm is not None:
          if (
              self.normalize_algorithm
              not in NORMALIZE_ALGORITHMS.keys()
          ):
              raise ValueError(
                  f"`normalize_algorithm` '{self.normalize_algorithm}' is not recognized. "
                  f"Must be one of {NORMALIZE_ALGORITHMS.keys()}"
              )
          if self.normalize_params is None:
              self.normalize_params = {}
          self.scaler = NORMALIZE_ALGORITHMS[self.normalize_algorithm](
              **self.normalize_params
          )
          self.scaler.fit(X=X)
      return self
  
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 2 for calling the NormalizeTransformer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self