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]])
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docstr: Perform standardization by centering and scaling.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_timestamps)
      Data to scale.
  
  Returns
  -------
  X_new : array-like, shape = (n_samples, n_timestamps)
      Scaled data.
docstr_tok: ["Perform","standardization","by","centering","and","scaling",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","(","n_samples",",","n_timestamps",")","Data","to","scale",".","Returns","--","--","--","-","X_new",":","array-like",",","shape","=","(","n_samples",",","n_timestamps",")","Scaled","data","."]
code: def transform(self, X):
      """Perform standardization by centering and scaling.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_timestamps)
          Data to scale.
  
      Returns
      -------
      X_new : array-like, shape = (n_samples, n_timestamps)
          Scaled data.
  
      """
      X = check_array(X, dtype="float64")
      scaler = SklearnStandardScaler(
          with_mean=self.with_mean, with_std=self.with_std
      )
      X_new = scaler.fit_transform(X.T).T
      return X_new
  
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 3 for calling the StandardScaler obj's underlying member method code with example usage: obj.transform(X) and returns: X_new