code: def set_args(largs, conv_bias=True, weights_regularizer=None):
      from .layers import conv2d
      from .layers import fc
      from .layers import sconv2d
  
      def real_set_args(func):
          def wrapper(*args, **kwargs):
              is_training = kwargs.get("is_training", False)
              layers = sum([x for (x, y) in largs(is_training)], [])
              layers_args = [
                  arg_scope(x, **y) for (x, y) in largs(is_training)
              ]
              if not conv_bias:
                  layers_args += [
                      arg_scope([conv2d], biases_initializer=None)
                  ]
              if weights_regularizer is not None:
                  layers_args += [
                      arg_scope(
                          [conv2d, fc, sconv2d],
                          weights_regularizer=weights_regularizer,
                      )
                  ]
              with arg_scope(layers, outputs_collections=__outputs__):
                  with arg_scopes(layers_args):
                      x = func(*args, **kwargs)
                      x.model_name = func.__name__
                      return x
  
          return wrapper
  
      return real_set_args
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  import warnings
  from contextlib import contextmanager
  from .version_utils import tf_later_than
label_desc: Use image node_id 22 for calling a global function with example usage: set_args(largs, conv_bias, weights_regularizer) and returns: real_set_args, wrapper, x