code: def hard_swish(features):
      """Computes a hard version of the swish function.
  
      This operation can be used to reduce computational cost and improve
      quantization for edge devices.
  
      Args:
        features: A `Tensor` representing preactivation values.
  
      Returns:
        The activation value.
      """
      features = tf.convert_to_tensor(features)
      return (
          features
          * tf.nn.relu6(features + tf.constant(3.0))
          * (1.0 / 6.0)
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: hard_swish(features) and returns: unknown