class_docstr: Scales each dimension into the interval [0, 1].
  
  Parameters
  ----------
  low : float
      Lower bound.
  
  high : float
      Higher bound.
  
  is_int : bool, default=False
      Round and cast the return value of `inverse_transform` to integer. Set
      to `True` when applying this transform to integers.
class_docstr_tok: ["Scales","each","dimension","into","the","interval","[","0",",","1","]",".","Parameters","--","--","--","--","--","low",":","float","Lower","bound",".","high",":","float","Higher","bound",".","is_int",":","bool",",","default=False","Round","and","cast","the","return","value","of","`","inverse_transform","`","to","integer",".","Set","to","`","True","`","when","applying","this","transform","to","integers","."]
code: def __init__(self, low, high, is_int=False):
      self.low = float(low)
      self.high = float(high)
      self.is_int = is_int
      self._eps = 1e-8
  
imports: from __future__ import division
  import numpy
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.utils import column_or_1d
label_desc: Use image node_id 1 to create a new Normalize object from inherited base classes: Transformer with example: obj = Normalize(low, high, is_int)