code: def enable_argscope_for_function(func, log_shape=True):
      
  
      assert callable(func), "func should be a callable"
  
      @wraps(func)
      def wrapped_func(*args, **kwargs):
          actual_args = copy.copy(get_arg_scope()[func.__name__])
          actual_args.update(kwargs)
          out_tensor = func(*args, **actual_args)
          in_tensor = args[0]
  
          ctx = get_current_tower_context()
          name = func.__name__ if 'name' not in kwargs else kwargs['name']
          if log_shape:
              if ('tower' not in ctx.ns_name.lower()) or ctx.is_main_training_tower:
                  # we assume the first parameter is the most interesting
                  if isinstance(out_tensor, tuple):
                      out_tensor_descr = out_tensor[0]
                  else:
                      out_tensor_descr = out_tensor
                  logger.info('%20s: %20s -> %20s' %
                              (name, in_tensor.shape.as_list(),
                               out_tensor_descr.shape.as_list()))
  
          return out_tensor
      # argscope requires this property
      wrapped_func.symbolic_function = None
      return wrapped_func
docs: Decorator for function to support argscope
  
      Example:
  
          .. code-block:: python
  
              from mylib import myfunc
              myfunc = enable_argscope_for_function(myfunc)
  
      Args:
          func: A function mapping one or multiple tensors to one or multiple
              tensors.
          log_shape (bool): Specify whether the first input resp. output tensor
              shape should be printed once.
  
      Remarks:
          If the function ``func`` returns multiple input or output tensors,
          only the first input/output tensor shape is displayed during logging.
  
      Returns:
          The decorated function.