class_docstr: Transforms samples by scaling each sample to a given range.
  
  Parameters
  ----------
  sample_range : tuple (min, max) (default = (0, 1))
      Desired range of transformed data.
  
  Examples
  --------
  >>> from pyts.preprocessing import MinMaxScaler
  >>> X = [[1, 5, 3, 2, 9, 6, 4, 7],
  ...      [1, -2, 3, 2, 2, 1, 0, 2]]
  >>> scaler = MinMaxScaler()
  >>> scaler.transform(X)
  array([[0.   , 0.5  , 0.25 , 0.125, 1.   , 0.625, 0.375, 0.75 ],
         [0.6  , 0.   , 1.   , 0.8  , 0.8  , 0.6  , 0.4  , 0.8  ]])
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code: def __init__(self, sample_range=(0, 1)):
      self.sample_range = sample_range
  
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 1 to create a new MinMaxScaler object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = MinMaxScaler(sample_range)