class_docstr: A preprocessing layer which rescales input values to a new range.
  
  This layer rescales every value of an input (often an image) by multiplying
  by `scale` and adding `offset`.
  
  For instance:
  
  1. To rescale an input in the `[0, 255]` range
  to be in the `[0, 1]` range, you would pass `scale=1./255`.
  
  2. To rescale an input in the `[0, 255]` range to be in the `[-1, 1]` range,
  you would pass `scale=1./127.5, offset=-1`.
  
  The rescaling is applied both during training and inference. Inputs can be
  of integer or floating point dtype, and by default the layer will output
  floats.
  
  **Note:** This layer is safe to use inside a `tf.data` pipeline
  (independently of which backend you're using).
  
  Args:
      scale: Float, the scale to apply to the inputs.
      offset: Float, the offset to apply to the inputs.
      **kwargs: Base layer keyword arguments, such as `name` and `dtype`.
code: def compute_output_shape(self, input_shape):
      return input_shape
  
file_path: keras/keras/layers/preprocessing/rescaling.py
imports: from keras import backend
  from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
label_desc: Use image node_id 3 for calling the Rescaling obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: input_shape