code: def leaky_relu6(x, alpha=0.2, name="leaky_relu6"):
      """:func:`leaky_relu6` can be used through its shortcut: :func:`tl.act.lrelu6`.
  
      This activation function is a modified version :func:`leaky_relu` introduced by the following paper:
      `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maas et al., 2013] <https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf>`__
  
      This activation function also follows the behaviour of the activation function :func:`tf.nn.relu6` introduced by the following paper:
      `Convolutional Deep Belief Networks on CIFAR-10 [A. Krizhevsky, 2010] <http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf>`__
  
      The function return the following results:
        - When x < 0: ``f(x) = alpha_low * x``.
        - When x in [0, 6]: ``f(x) = x``.
        - When x > 6: ``f(x) = 6``.
  
      Parameters
      ----------
      x : Tensor
          Support input type ``float``, ``double``, ``int32``, ``int64``, ``uint8``, ``int16``, or ``int8``.
      alpha : float
          Slope.
      name : str
          The function name (optional).
  
      Examples
      --------
      >>> import tensorlayer as tl
      >>> net = tl.layers.Input([10, 200])
      >>> net = tl.layers.Dense(n_units=100, act=lambda x : tl.act.leaky_relu6(x, 0.2), name='dense')(net)
  
      Returns
      -------
      Tensor
          A ``Tensor`` in the same type as ``x``.
  
      References
      ----------
      - `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maas et al., 2013] <https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf>`__
      - `Convolutional Deep Belief Networks on CIFAR-10 [A. Krizhevsky, 2010] <http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf>`__
      """
      if not isinstance(alpha, tf.Tensor) and not (0 < alpha <= 1):
          raise ValueError("`alpha` value must be in [0, 1]`")
  
      with tf.name_scope(name) as name_scope:
          x = tf.convert_to_tensor(x, name="features")
          return tf.minimum(
              tf.maximum(x, alpha * x), 6, name=name_scope
          )
  
file_path: TensorLayer/tensorlayer/activation.py
imports: import tensorflow
  from tensorlayer.decorators import deprecated
label_desc: Use image node_id 3 for calling a global function with example usage: leaky_relu6(x, alpha, name) and returns: tf