class_docstr: Standardize time series by removing mean and scaling to unit variance.
  
  Parameters
  ----------
  with_mean : bool (default = True)
      If True, center the data before scaling.
  
  with_std : bool (default = True)
      If True, scale the data to unit variance.
  
  Examples
  --------
  >>> from pyts.preprocessing import StandardScaler
  >>> X = [[0, 2, 0, 4, 4, 6, 4, 4],
  ...      [1, 0, 3, 2, 2, 2, 0, 2]]
  >>> scaler = StandardScaler()
  >>> scaler.transform(X)
  array([[-1.5, -0.5, -1.5,  0.5,  0.5,  1.5,  0.5,  0.5],
         [-0.5, -1.5,  1.5,  0.5,  0.5,  0.5, -1.5,  0.5]])
class_docstr_tok: ["Standardize","time","series","by","removing","mean","and","scaling","to","unit","variance",".","Parameters","--","--","--","--","--","with_mean",":","bool","(","default","=","True",")","If","True",",","center","the","data","before","scaling",".","with_std",":","bool","(","default","=","True",")","If","True",",","scale","the","data","to","unit","variance",".","Examples","--","--","--","--",">",">",">","from","pyts.preprocessing","import","StandardScaler",">",">",">","X","=","[","[","0",",","2",",","0",",","4",",","4",",","6",",","4",",","4","]",",","...","[","1",",","0",",","3",",","2",",","2",",","2",",","0",",","2","]","]",">",">",">","scaler","=","StandardScaler","(",")",">",">",">","scaler.transform","(","X",")","array","(","[","[","-1.5",",","-0.5",",","-1.5",",","0.5",",","0.5",",","1.5",",","0.5",",","0.5","]",",","[","-0.5",",","-1.5",",","1.5",",","0.5",",","0.5",",","0.5",",","-1.5",",","0.5","]","]",")"]
docstr: Pass.
  
  Parameters
  ----------
  X
      Ignored
  
  y
      Ignored
  
  Returns
  -------
  self : object
docstr_tok: ["Pass",".","Parameters","--","--","--","--","--","X","Ignored","y","Ignored","Returns","--","--","--","-","self",":","object"]
code: def fit(self, X=None, y=None):
      """Pass.
  
      Parameters
      ----------
      X
          Ignored
  
      y
          Ignored
  
      Returns
      -------
      self : object
  
      """
      return self
  
imports: from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import MaxAbsScaler
  from sklearn.preprocessing import RobustScaler
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 2 for calling the StandardScaler obj's underlying member method code with example usage: obj.fit(X, y) and returns: self