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 inverse_transform(self, X):
      X = np.asarray(X)
      if np.any(X > 1.0 + self._eps):
          raise ValueError("All values should be less than 1.0")
      if np.any(X < 0.0 - self._eps):
          raise ValueError("All values should be greater than 0.0")
      X_orig = X * (self.high - self.low) + self.low
      if self.is_int:
          return np.round(X_orig).astype(np.int)
      return X_orig
  
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 3 for calling the Normalize obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X_orig, np