class_docstr: Scale each sample by its maximum absolute value.
  
  Examples
  --------
  >>> from pyts.preprocessing import MaxAbsScaler
  >>> X = [[1, 5, 3, 2, 10, 6, 4, 7],
  ...      [1, -5, 3, 2, 2, 1, 0, 2]]
  >>> scaler = MaxAbsScaler()
  >>> scaler.transform(X)
  array([[ 0.1,  0.5,  0.3,  0.2,  1. ,  0.6,  0.4,  0.7],
         [ 0.2, -1. ,  0.6,  0.4,  0.4,  0.2,  0. ,  0.4]])
class_docstr_tok: ["Scale","each","sample","by","its","maximum","absolute","value",".","Examples","--","--","--","--",">",">",">","from","pyts.preprocessing","import","MaxAbsScaler",">",">",">","X","=","[","[","1",",","5",",","3",",","2",",","10",",","6",",","4",",","7","]",",","...","[","1",",","-5",",","3",",","2",",","2",",","1",",","0",",","2","]","]",">",">",">","scaler","=","MaxAbsScaler","(",")",">",">",">","scaler.transform","(","X",")","array","(","[","[","0.1",",","0.5",",","0.3",",","0.2",",","1.",",","0.6",",","0.4",",","0.7","]",",","[","0.2",",","-1.",",","0.6",",","0.4",",","0.4",",","0.2",",","0.",",","0.4","]","]",")"]
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 MaxAbsScaler obj's underlying member method code with example usage: obj.fit(X, y) and returns: self