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 load(self, kwargs):
      # Flip and Rotate options
      add_parameter(self, kwargs, "shift", True)
      add_parameter(self, kwargs, "squeeze", True)
      add_parameter(self, kwargs, "shift_amount", [-0.5, 0.5])
      add_parameter(self, kwargs, "squeeze_factor", [0.7, 1.3])
  
      # TODO: This is incredibly over-elaborate, return to fix.
      self.transforms_list = []
  
      if self.shift:
          self.shift_list = [False, True]
      else:
          self.shift_list = [False]
  
      if self.squeeze:
          self.squeeze_list = [False, True]
      else:
          self.squeeze_list = [False]
  
      self.available_transforms = np.array(
          np.meshgrid(self.shift_list, self.squeeze_list)
      ).T.reshape(-1, 2)
      self.total_transforms = self.available_transforms.shape[0]
      self.augmentation_string = "_shift_squeeze_"
  
imports: import numpy
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 1 for calling the Shift_Squeeze_Intensities obj's underlying member method code with example usage: obj.load(kwargs) without return types