Method wrapped_fn

tensorflow/python/eager/def_function.py:318–335

Wraps `self._python_function` in a variable creator scope.

(*args, **kwds)

    weak_wrapped_fn = None
    def wrapped_fn(*args, **kwds):
      """Wraps `self._python_function` in a variable creator scope."""
      # We register a variable creator with reduced priority. If an outer
      # variable creator is just modifying keyword arguments to the variable
      # constructor, this will work harmoniously. Since the `scope` registered
      # here actually creates the variable, it taking priority would otherwise
      # ignore the outer creator.
      #
      # If an outer variable creator calls the variable constructor manually,
      # for example creating a MirroredVariable, then they won't call our
      # creator. This means we won't be able to trace the initialization graph,
      # and so variable initializers can't depend on function arguments. This is
      # better than the alternative, tracing the initialization graph but giving
      # the user a variable type they didn't want.
      with ops.get_default_graph()._variable_creator_scope(scope, priority=50):  # pylint: disable=protected-access
        # __wrapped__ allows AutoGraph to swap in a converted function. We give
        # the function a weak reference to itself to avoid a reference cycle.
        return weak_wrapped_fn().__wrapped__(*args, **kwds)
    weak_wrapped_fn = weakref.ref(wrapped_fn)

    return self._defun(tf_decorator.make_decorator(