code: def hard_sigmoid(x):
      """Hard sigmoid activation function.
  
      A faster approximation of the sigmoid activation.
      Piecewise linear approximation of the sigmoid function.
      Ref: 'https://en.wikipedia.org/wiki/Hard_sigmoid'
  
      For example:
  
      >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
      >>> b = tf.keras.activations.hard_sigmoid(a)
      >>> b.numpy()
      array([0. , 0.3, 0.5, 0.7, 1. ], dtype=float32)
  
      Args:
          x: Input tensor.
  
      Returns:
        The hard sigmoid activation, defined as:
  
          - `if x < -2.5: return 0`
          - `if x > 2.5: return 1`
          - `if -2.5 <= x <= 2.5: return 0.2 * x + 0.5`
      """
      return backend.hard_sigmoid(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 12 for calling a global function with example usage: hard_sigmoid(x) and returns: backend