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: Normalizes data using the specified scaling method.
  
  Parameters
  ----------
  X : `pandas.DataFrame`
      Data to transform. e.g. each column is a timeseries.
      Columns are expected to be numeric.
  
  Returns
  -------
  X_normalized : `pandas.DataFrame`
      A normalized copy of the data frame.
docstr_tok: ["Normalizes","data","using","the","specified","scaling","method",".","Parameters","--","--","--","--","--","X",":","`","pandas.DataFrame","`","Data","to","transform",".","e.g",".","each","column","is","a","timeseries",".","Columns","are","expected","to","be","numeric",".","Returns","--","--","--","-","X_normalized",":","`","pandas.DataFrame","`","A","normalized","copy","of","the","data","frame","."]
code: def transform(self, X):
      """Normalizes data using the specified scaling method.
  
      Parameters
      ----------
      X : `pandas.DataFrame`
          Data to transform. e.g. each column is a timeseries.
          Columns are expected to be numeric.
  
      Returns
      -------
      X_normalized : `pandas.DataFrame`
          A normalized copy of the data frame.
      """
      if self._is_fitted is None:
          raise NotFittedError(
              "This instance is not fitted yet. Call 'fit' with appropriate arguments "
              "before calling 'transform'."
          )
      assert isinstance(X, pd.DataFrame)
      if self.scaler:
          transformed = self.scaler.transform(X)
          X_normalized = pd.DataFrame(
              transformed, index=X.index, columns=X.columns
          )
      else:
          X_normalized = X.copy()
      return X_normalized
  
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 3 for calling the NormalizeTransformer obj's underlying member method code with example usage: obj.transform(X) and returns: X_normalized