code: def normalize(self, normalize_numpy, mask_numpy=None):
      normalize_numpy = normalize_numpy.astype(float)
  
      if mask_numpy is not None:
          mask = mask_numpy > 0
      elif self.mask_zeros:
          mask = np.nonzero(normalize_numpy)
      else:
          mask = None
  
      if mask is None:
          if self.input_intensity_range is None:
              input_intensity_range = [
                  np.min(normalize_numpy),
                  np.max(normalize_numpy),
              ]
          else:
              input_intensity_range = self.input_intensity_range
  
          # Edge Case -- Minimum and Maximum
          if input_intensity_range[0] == input_intensity_range[1]:
              normalize_numpy[:] = self.intensity_range[0]
              print(
                  "Warning: normalization edge case. All array values are equal. Normalizing to minimum value."
              )
  
          else:
              normalize_numpy = (
                  (self.intensity_range[1] - self.intensity_range[0])
                  * (normalize_numpy - input_intensity_range[0])
              ) / (
                  input_intensity_range[1] - input_intensity_range[0]
              ) + self.intensity_range[
                  0
              ]
  
          if self.input_intensity_range is not None:
              normalize_numpy[
                  normalize_numpy < self.intensity_range[0]
              ] = self.intensity_range[0]
              normalize_numpy[
                  normalize_numpy > self.intensity_range[1]
              ] = self.intensity_range[1]
  
      else:
          raise NotImplementedError
  
      return normalize_numpy
  
imports: import subprocess
  import os
  import numpy
  from deepneuro.preprocessing.preprocessor import Preprocessor
  from deepneuro.utilities.conversion import read_image_files
  from deepneuro.utilities.util import add_parameter, quotes
label_desc: Use image node_id 2 for calling the RangeNormalization obj's underlying member method code with example usage: obj.normalize(normalize_numpy, mask_numpy) and returns: normalize_numpy