title: Can a TensorFlow Hub module be used in TensorFlow 2.0?
body: <p>I tried running this code in TensorFlow 2.0 (alpha):</p>
  
  <pre class="lang-py prettyprint-override"><code>import tensorflow_hub as hub
  
  @tf.function
  def elmo(texts):
      elmo_module = hub.Module("https://tfhub.dev/google/elmo/2", trainable=True)
      return elmo_module(texts, signature="default", as_dict=True)
  
  embeds = elmo(tf.constant(["the cat is on the mat",
                             "dogs are in the fog"]))
  </code></pre>
  
  <p>But I got this error:</p>
  
  <pre><code>---------------------------------------------------------------------------
  RuntimeError                              Traceback (most recent call last)
  &lt;ipython-input-1-c7f14c7ed0e9&gt; in &lt;module&gt;
        9
       10 elmo(tf.constant(["the cat is on the mat",
  ---&gt; 11                   "dogs are in the fog"]))
  
  .../tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
      417     # This is the first call of __call__, so we have to initialize.
      418     initializer_map = {}
  --&gt; 419     self._initialize(args, kwds, add_initializers_to=initializer_map)
      420     if self._created_variables:
      421       try:
  
  .../tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
      361     self._concrete_stateful_fn = (
      362         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
  --&gt; 363             *args, **kwds))
      364
      365     def invalid_creator_scope(*unused_args, **unused_kwds):
  
  .../tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
     1322     if self.input_signature:
     1323       args, kwargs = None, None
  -&gt; 1324     graph_function, _, _ = self._maybe_define_function(args, kwargs)
     1325     return graph_function
     1326
  
  .../tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
     1585           or call_context_key not in self._function_cache.missed):
     1586         self._function_cache.missed.add(call_context_key)
  -&gt; 1587         graph_function = self._create_graph_function(args, kwargs)
     1588         self._function_cache.primary[cache_key] = graph_function
     1589         return graph_function, args, kwargs
  
  .../tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
     1518             arg_names=arg_names,
     1519             override_flat_arg_shapes=override_flat_arg_shapes,
  -&gt; 1520             capture_by_value=self._capture_by_value),
     1521         self._function_attributes)
     1522
  
  .../tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
      705                                           converted_func)
      706
  --&gt; 707       func_outputs = python_func(*func_args, **func_kwargs)
      708
      709       # invariant: `func_outputs` contains only Tensors, IndexedSlices,
  
  .../tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
      314         # __wrapped__ allows AutoGraph to swap in a converted function. We give
      315         # the function a weak reference to itself to avoid a reference cycle.
  --&gt; 316         return weak_wrapped_fn().__wrapped__(*args, **kwds)
      317     weak_wrapped_fn = weakref.ref(wrapped_fn)
      318
  
  .../tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
      697                   optional_features=autograph_options,
      698                   force_conversion=True,
  --&gt; 699               ), args, kwargs)
      700
      701         # Wrapping around a decorator allows checks like tf_inspect.getargspec
  
  .../tensorflow/python/autograph/impl/api.py in converted_call(f, owner, options, args, kwargs)
      355
      356   if kwargs is not None:
  --&gt; 357     result = converted_f(*effective_args, **kwargs)
      358   else:
      359     result = converted_f(*effective_args)
  
  /var/folders/wy/h39t6kb11pnbb0pzhksd_fqh0000gn/T/tmp4v3g2d_1.py in tf__elmo(texts)
       11       retval_ = None
       12       print('Eager:', ag__.converted_call('executing_eagerly', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (), None))
  ---&gt; 13       elmo_module = ag__.converted_call('Module', hub, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), ('https://tfhub.dev/google/elmo/2',), {'trainable': True})
       14       do_return = True
       15       retval_ = ag__.converted_call(elmo_module, None, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (texts,), {'signature': 'default', 'as_dict': True})
  
  .../tensorflow/python/autograph/impl/api.py in converted_call(f, owner, options, args, kwargs)
      252   if tf_inspect.isclass(f):
      253     logging.log(2, 'Permanently whitelisted: %s: constructor', f)
  --&gt; 254     return _call_unconverted(f, args, kwargs)
      255
      256   # Other built-in modules are permanently whitelisted.
  
  .../tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs)
      174
      175   if kwargs is not None:
  --&gt; 176     return f(*args, **kwargs)
      177   else:
      178     return f(*args)
  
  .../tensorflow_hub/module.py in __init__(self, spec, trainable, name, tags)
      167           name=self._name,
      168           trainable=self._trainable,
  --&gt; 169           tags=self._tags)
      170       # pylint: enable=protected-access
      171
  
  .../tensorflow_hub/native_module.py in _create_impl(self, name, trainable, tags)
      338         trainable=trainable,
      339         checkpoint_path=self._checkpoint_variables_path,
  --&gt; 340         name=name)
      341
      342   def _export(self, path, variables_saver):
  
  .../tensorflow_hub/native_module.py in __init__(self, spec, meta_graph, trainable, checkpoint_path, name)
      389     # TPU training code.
      390     with tf.init_scope():
  --&gt; 391       self._init_state(name)
      392
      393   def _init_state(self, name):
  
  .../tensorflow_hub/native_module.py in _init_state(self, name)
      392
      393   def _init_state(self, name):
  --&gt; 394     variable_tensor_map, self._state_map = self._create_state_graph(name)
      395     self._variable_map = recover_partitioned_variable_map(
      396         get_node_map_from_tensor_map(variable_tensor_map))
  
  .../tensorflow_hub/native_module.py in _create_state_graph(self, name)
      449         meta_graph,
      450         input_map={},
  --&gt; 451         import_scope=relative_scope_name)
      452
      453     # Build a list from the variable name in the module definition to the actual
  
  .../tensorflow/python/training/saver.py in import_meta_graph(meta_graph_or_file, clear_devices, import_scope, **kwargs)
     1443   """  # pylint: disable=g-doc-exception
     1444   return _import_meta_graph_with_return_elements(
  -&gt; 1445       meta_graph_or_file, clear_devices, import_scope, **kwargs)[0]
     1446
     1447
  
  .../tensorflow/python/training/saver.py in _import_meta_graph_with_return_elements(meta_graph_or_file, clear_devices, import_scope, return_elements, **kwargs)
     1451   """Import MetaGraph, and return both a saver and returned elements."""
     1452   if context.executing_eagerly():
  -&gt; 1453     raise RuntimeError("Exporting/importing meta graphs is not supported when "
     1454                        "eager execution is enabled. No graph exists when eager "
     1455                        "execution is enabled.")
  
  RuntimeError: Exporting/importing meta graphs is not supported when eager execution is enabled. No graph exists when eager execution is enabled.
  </code></pre>