code: def simple_swish(features):
      """Computes the Swish activation function.
  
      The tf.nn.swish operation uses a custom gradient to reduce memory usage.
      Since saving custom gradients in SavedModel is currently not supported, and
      one would not be able to use an exported TF-Hub module for fine-tuning, we
      provide this wrapper that can allow to select whether to use the native
      TensorFlow swish operation, or whether to use a customized operation that
      has uses default TensorFlow gradient computation.
  
      Args:
        features: A `Tensor` representing preactivation values.
  
      Returns:
        The activation value.
      """
      features = tf.convert_to_tensor(features)
      return features * tf.nn.sigmoid(features)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: simple_swish(features) and returns: unknown