code: def lo_regularizer(scale):
      """Lo regularization removes the neurons of current layer. The `o` represents `outputs`
      Returns a function that can be used to apply group lo regularization to weights.
      The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.
  
      Parameters
      ----------
      scale : float
          A scalar multiplier `Tensor`. 0.0 disables the regularizer.
  
      Returns
      -------
      A function with signature `lo(weights, name=None)` that apply Lo regularization.
  
      Raises
      ------
      ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
  
      """
      if isinstance(scale, numbers.Integral):
          raise ValueError("scale cannot be an integer: %s" % scale)
  
      if isinstance(scale, numbers.Real):
          if scale < 0.0:
              raise ValueError(
                  "Setting a scale less than 0 on a regularizer: %g"
                  % scale
              )
          if scale >= 1.0:
              raise ValueError(
                  "Setting a scale greater than 1 on a regularizer: %g"
                  % scale
              )
          if scale == 0.0:
              logging.info("Scale of 0 disables regularizer.")
              return lambda _, name=None: None
  
      def lo(weights, name="lo_regularizer"):
          """Applies group column regularization to weights."""
          with tf.name_scope(name) as scope:
              my_scale = ops.convert_to_tensor(
                  scale, dtype=weights.dtype.base_dtype, name="scale"
              )
              # if tf.__version__ <= '0.12':
              #     standard_ops_fn = standard_ops.mul
              # else:
              standard_ops_fn = standard_ops.multiply
              return standard_ops_fn(
                  my_scale,
                  standard_ops.reduce_sum(
                      standard_ops.sqrt(
                          standard_ops.reduce_sum(tf.square(weights), 0)
                      )
                  ),
                  name=scope,
              )
  
      return lo
  
imports: import numbers
  import tensorflow
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops, math_ops, nn_ops, standard_ops
  from tensorlayer import logging
label_desc: Use image node_id 15 for calling a global function with example usage: lo_regularizer(scale) and returns: lo, function, standard_ops_fn