old_contents: #! /usr/bin/python
  # -*- coding: utf-8 -*-
  
  import tensorflow as tf
  from tensorlayer import logging
  from tensorlayer.initializers import constant
  from tensorlayer.layers.core import Layer
  
  __all__ = [
      'Scale',
  ]
  
  
  class Scale(Layer):
      """The :class:`Scale` class is to multiple a trainable scale value to the layer outputs. Usually be used on the output of binary net.
  
      Parameters
      ----------
      init_scale : float
          The initial value for the scale factor.
      name : a str
          A unique layer name.
  
      Examples:
      ----------
      >>> inputs = tl.layers.Input([8, 3])
      >>> dense = tl.layers.Dense(n_units=10)(inputs)
      >>> outputs = tl.layers.Scale(init_scale=0.5)(dense)
      >>> model = tl.models.Model(inputs=inputs, outputs=[dense, outputs])
      >>> dense_out, scale_out = model(data, is_train=True)
  
      """
  
      def __init__(
              self,
              init_scale=0.05,
              name='scale',
      ):
          super(Scale, self).__init__(name)
          self.init_scale = init_scale
  
          self.build((None, ))
          self._built = True
  
          logging.info("Scale  %s: init_scale: %f" % (self.name, self.init_scale))
  
      def __repr__(self):
          s = '{classname}('
          s += 'init_scale={init_scale},'
          s += 'name={name}'
          s += ")"
          return s.format(classname=self.__class__.__name__, **self.__dict__)
  
      def build(self, inputs_shape):
          self.scale = self._get_weights("scale", shape=[1], init=constant(value=self.init_scale))
  
      # @tf.function
      def forward(self, inputs):
          outputs = inputs * self.scale
          return outputs
  
new_contents: #! /usr/bin/python
  # -*- coding: utf-8 -*-
  
  import tensorflow as tf
  from tensorlayer import logging
  from tensorlayer.initializers import constant
  from tensorlayer.layers.core import Layer
  
  __all__ = [
      'Scale',
  ]
  
  
  class Scale(Layer):
      """The :class:`Scale` class is to multiple a trainable scale value to the layer outputs. Usually be used on the output of binary net.
  
      Parameters
      ----------
      init_scale : float
          The initial value for the scale factor.
      name : a str
          A unique layer name.
  
      Examples
      ----------
      >>> inputs = tl.layers.Input([8, 3])
      >>> dense = tl.layers.Dense(n_units=10)(inputs)
      >>> outputs = tl.layers.Scale(init_scale=0.5)(dense)
      >>> model = tl.models.Model(inputs=inputs, outputs=[dense, outputs])
      >>> dense_out, scale_out = model(data, is_train=True)
  
      """
  
      def __init__(
              self,
              init_scale=0.05,
              name='scale',
      ):
          super(Scale, self).__init__(name)
          self.init_scale = init_scale
  
          self.build((None, ))
          self._built = True
  
          logging.info("Scale  %s: init_scale: %f" % (self.name, self.init_scale))
  
      def __repr__(self):
          s = '{classname}('
          s += 'init_scale={init_scale},'
          s += 'name={name}'
          s += ")"
          return s.format(classname=self.__class__.__name__, **self.__dict__)
  
      def build(self, inputs_shape):
          self.scale = self._get_weights("scale", shape=[1], init=constant(value=self.init_scale))
  
      # @tf.function
      def forward(self, inputs):
          outputs = inputs * self.scale
          return outputs
  
subject: Update scale.py