class_docstr: TODO: extend to be more flexible and useful.
  Ponder about how best to apply to multiple dimensions
class_docstr_tok: ["TODO",":","extend","to","be","more","flexible","and","useful",".","Ponder","about","how","best","to","apply","to","multiple","dimensions"]
code: def augment(self, augmentation_num=0):
      for label, data_group in list(self.data_groups.items()):
          if self.available_transforms[
              self.iteration % self.total_transforms, 0
          ]:
              data_group.augmentation_cases[
                  augmentation_num + 1
              ] = data_group.augmentation_cases[
                  augmentation_num
              ] + np.random.uniform(
                  self.shift_amount[0], self.shift_amount[1]
              )
          else:
              data_group.augmentation_cases[
                  augmentation_num + 1
              ] = data_group.augmentation_cases[augmentation_num]
  
          if self.available_transforms[
              self.iteration % self.total_transforms, 0
          ]:
              data_group.augmentation_cases[
                  augmentation_num + 1
              ] = data_group.augmentation_cases[
                  augmentation_num
              ] * np.random.uniform(
                  self.squeeze_factor[0], self.squeeze_factor[1]
              )
          else:
              data_group.augmentation_cases[
                  augmentation_num + 1
              ] = data_group.augmentation_cases[augmentation_num]
  
imports: import numpy
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 2 for calling the Shift_Squeeze_Intensities obj's underlying member method code with example usage: obj.augment(augmentation_num) without return types