code: def leaky_relu(x, name=None):
    
    with tf.name_scope(name, 'leaky_relu', [x]) as scope:
      x = tf.convert_to_tensor(x, name='x')
      return tf.where(tf.less(x, 0.0), 0.01 * x, x, name=scope)
docs: Creates a leaky_relu.
  
    This is an alternate non-linearity to relu. The leaky part of the relu may
    prevent dead Neurons in a model since the gradient doesn't go completely to
    0.
  
    Args:
      x: The input tensor.
      name: Optional name for this op.
    Returns:
      x if x > 0 otherwise 0.01 * x.