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