code: def swish(x):
      """Swish activation function, `swish(x) = x * sigmoid(x)`.
  
      Swish activation function which returns `x*sigmoid(x)`.
      It is a smooth, non-monotonic function that consistently matches
      or outperforms ReLU on deep networks, it is unbounded above and
      bounded below.
  
  
      Example Usage:
  
      >>> a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32)
      >>> b = tf.keras.activations.swish(a)
      >>> b.numpy()
      array([-4.1223075e-08, -2.6894143e-01,  0.0000000e+00,  7.3105860e-01,
                2.0000000e+01], dtype=float32)
  
      Args:
          x: Input tensor.
  
      Returns:
          The swish activation applied to `x` (see reference paper for details).
  
      Reference:
        - [Ramachandran et al., 2017](https://arxiv.org/abs/1710.05941)
      """
      return nn.swish(x)
  
imports: from tensorflow.python.keras import backend
  from tensorflow.python.keras.layers import advanced_activations
  from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
  from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.util import dispatch
label_desc: Use image node_id 6 for calling a global function with example usage: swish(x) and returns: nn