code: def hard_sigmoid(x):
      """Hard Sigmoid.
  
      Segment-wise linear approximation of sigmoid. Faster than sigmoid
  
      Arguments:
        x: Input tensor.
  
      Returns:
        Hard sigmoid activation:
  
        - `0` if `x < -2.5`
        - `1` if `x > 2.5`
        - `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`.
  
      """
      return tf.keras.backend.hard_sigmoid(x)
  
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  import numpy
  import tflearn
  from .None import initializations
  from .None import variables
  from .utils import get_from_module
label_desc: Use image node_id 15 for calling a global function with example usage: hard_sigmoid(x) and returns: tf