TypeError: Expected Operation, Variable, or Tensor, got None while saving tensorflow model
## 🐛 Bug

<!-- Important information -->

Model I am using (Bert, XLNet....): TFAlbert

Language I am using the model on (English, Chinese....): English

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name): GLUE
* [ ] my own task or dataset: (give details)

## Environment

* OS: Linux
* Python version: Python 3.7.5 /
* Tensorflow version: 2.0.0
* Using GPU ? GPU

**System information**
- Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux
- TensorFlow installed from (source or binary): Source
- TensorFlow version: '2.0.0'
- Python version: Python 3.7.5 /Conda 
- CUDA/cuDNN version: cuda10.0_0/cudnn-7.6.5
- GPU model and memory: Tesla V100-PCIE / 32 GB memory



**Describe the current behavior**
**I am getting TypeError: Expected Operation, Variable, or Tensor, got None while saving the model using model.save('../output/my_model')**

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-49-5ab71d0ebc23> in <module>
----> 1 model.save('../output/my_model')

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
    973     """
    974     saving.save_model(self, filepath, overwrite, include_optimizer, save_format,
--> 975                       signatures, options)
    976 
    977   def save_weights(self, filepath, overwrite=True, save_format=None):

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
    113   else:
    114     saved_model_save.save(model, filepath, overwrite, include_optimizer,
--> 115                           signatures, options)
    116 
    117 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save.py in save(model, filepath, overwrite, include_optimizer, signatures, options)
     72   # default learning phase placeholder.
     73   with K.learning_phase_scope(0):
---> 74     save_lib.save(model, filepath, signatures, options)
     75 
     76   if not include_optimizer:

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/saved_model/save.py in save(obj, export_dir, signatures, options)
    868   if signatures is None:
    869     signatures = signature_serialization.find_function_to_export(
--> 870         checkpoint_graph_view)
    871 
    872   signatures = signature_serialization.canonicalize_signatures(signatures)

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/saved_model/signature_serialization.py in find_function_to_export(saveable_view)
     62   # If the user did not specify signatures, check the root object for a function
     63   # that can be made into a signature.
---> 64   functions = saveable_view.list_functions(saveable_view.root)
     65   signature = functions.get(DEFAULT_SIGNATURE_ATTR, None)
     66   if signature is not None:

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/saved_model/save.py in list_functions(self, obj)
    139     if obj_functions is None:
    140       obj_functions = obj._list_functions_for_serialization(  # pylint: disable=protected-access
--> 141           self._serialization_cache)
    142       self._functions[obj] = obj_functions
    143     return obj_functions

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in _list_functions_for_serialization(self, serialization_cache)
   2420   def _list_functions_for_serialization(self, serialization_cache):
   2421     return (self._trackable_saved_model_saver
-> 2422             .list_functions_for_serialization(serialization_cache))
   2423 
   2424 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/base_serialization.py in list_functions_for_serialization(self, serialization_cache)
     89         `ConcreteFunction`.
     90     """
---> 91     fns = self.functions_to_serialize(serialization_cache)
     92 
     93     # The parent AutoTrackable class saves all user-defined tf.functions, and

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in functions_to_serialize(self, serialization_cache)
     77   def functions_to_serialize(self, serialization_cache):
     78     return (self._get_serialized_attributes(
---> 79         serialization_cache).functions_to_serialize)
     80 
     81   def _get_serialized_attributes(self, serialization_cache):

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in _get_serialized_attributes(self, serialization_cache)
     92 
     93     object_dict, function_dict = self._get_serialized_attributes_internal(
---> 94         serialization_cache)
     95 
     96     serialized_attr.set_and_validate_objects(object_dict)

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/model_serialization.py in _get_serialized_attributes_internal(self, serialization_cache)
     45     # cache (i.e. this is the root level object).
     46     if len(serialization_cache[constants.KERAS_CACHE_KEY]) == 1:
---> 47       default_signature = save_impl.default_save_signature(self.obj)
     48 
     49     # Other than the default signature function, all other attributes match with

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in default_save_signature(layer)
    204   original_losses = _reset_layer_losses(layer)
    205   fn = saving_utils.trace_model_call(layer)
--> 206   fn.get_concrete_function()
    207   _restore_layer_losses(original_losses)
    208   return fn

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in get_concrete_function(self, *args, **kwargs)
    774       if self._stateful_fn is None:
    775         initializer_map = object_identity.ObjectIdentityDictionary()
--> 776         self._initialize(args, kwargs, add_initializers_to=initializer_map)
    777         self._initialize_uninitialized_variables(initializer_map)
    778 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    406     self._concrete_stateful_fn = (
    407         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 408             *args, **kwds))
    409 
    410     def invalid_creator_scope(*unused_args, **unused_kwds):

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   1846     if self.input_signature:
   1847       args, kwargs = None, None
-> 1848     graph_function, _, _ = self._maybe_define_function(args, kwargs)
   1849     return graph_function
   1850 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   2148         graph_function = self._function_cache.primary.get(cache_key, None)
   2149         if graph_function is None:
-> 2150           graph_function = self._create_graph_function(args, kwargs)
   2151           self._function_cache.primary[cache_key] = graph_function
   2152         return graph_function, args, kwargs

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   2039             arg_names=arg_names,
   2040             override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041             capture_by_value=self._capture_by_value),
   2042         self._function_attributes,
   2043         # Tell the ConcreteFunction to clean up its graph once it goes out of

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/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)
    913                                           converted_func)
    914 
--> 915       func_outputs = python_func(*func_args, **func_kwargs)
    916 
    917       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    356         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    357         # the function a weak reference to itself to avoid a reference cycle.
--> 358         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    359     weak_wrapped_fn = weakref.ref(wrapped_fn)
    360 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saving_utils.py in _wrapped_model(*args)
    141     with base_layer_utils.call_context().enter(
    142         model, inputs=inputs, build_graph=False, training=False, saving=True):
--> 143       outputs_list = nest.flatten(model(inputs=inputs, training=False))
    144 
    145     try:

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    845                     outputs = base_layer_utils.mark_as_return(outputs, acd)
    846                 else:
--> 847                   outputs = call_fn(cast_inputs, *args, **kwargs)
    848 
    849             except errors.OperatorNotAllowedInGraphError as e:

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    290   def wrapper(*args, **kwargs):
    291     with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292       return func(*args, **kwargs)
    293 
    294   if inspect.isfunction(func) or inspect.ismethod(func):

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/transformers/modeling_tf_albert.py in call(self, inputs, **kwargs)
    783 
    784     def call(self, inputs, **kwargs):
--> 785         outputs = self.albert(inputs, **kwargs)
    786 
    787         pooled_output = outputs[1]

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    845                     outputs = base_layer_utils.mark_as_return(outputs, acd)
    846                 else:
--> 847                   outputs = call_fn(cast_inputs, *args, **kwargs)
    848 
    849             except errors.OperatorNotAllowedInGraphError as e:

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    290   def wrapper(*args, **kwargs):
    291     with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292       return func(*args, **kwargs)
    293 
    294   if inspect.isfunction(func) or inspect.ismethod(func):

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/transformers/modeling_tf_albert.py in call(self, inputs, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, training)
    680 
    681         embedding_output = self.embeddings(
--> 682             [input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
    683         encoder_outputs = self.encoder(
    684             [embedding_output, extended_attention_mask, head_mask], training=training)

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    889           with base_layer_utils.autocast_context_manager(
    890               self._compute_dtype):
--> 891             outputs = self.call(cast_inputs, *args, **kwargs)
    892           self._handle_activity_regularization(inputs, outputs)
    893           self._set_mask_metadata(inputs, outputs, input_masks)

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in return_outputs_and_add_losses(*args, **kwargs)
     55     inputs = args[inputs_arg_index]
     56     args = args[inputs_arg_index + 1:]
---> 57     outputs, losses = fn(inputs, *args, **kwargs)
     58     layer.add_loss(losses, inputs)
     59     return outputs

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in wrap_with_training_arg(*args, **kwargs)
    109         training,
    110         lambda: replace_training_and_call(True),
--> 111         lambda: replace_training_and_call(False))
    112 
    113   # Create arg spec for decorated function. If 'training' is not defined in the

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/tf_utils.py in smart_cond(pred, true_fn, false_fn, name)
     57         pred, true_fn=true_fn, false_fn=false_fn, name=name)
     58   return smart_module.smart_cond(
---> 59       pred, true_fn=true_fn, false_fn=false_fn, name=name)
     60 
     61 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/framework/smart_cond.py in smart_cond(pred, true_fn, false_fn, name)
     54       return true_fn()
     55     else:
---> 56       return false_fn()
     57   else:
     58     return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn,

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in <lambda>()
    109         training,
    110         lambda: replace_training_and_call(True),
--> 111         lambda: replace_training_and_call(False))
    112 
    113   # Create arg spec for decorated function. If 'training' is not defined in the

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in replace_training_and_call(training)
    104     def replace_training_and_call(training):
    105       set_training_arg(training, training_arg_index, args, kwargs)
--> 106       return wrapped_call(*args, **kwargs)
    107 
    108     return tf_utils.smart_cond(

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in __call__(self, *args, **kwargs)
    531     if not self.call_collection.tracing:
    532       self.call_collection.add_trace(*args, **kwargs)
--> 533     return super(LayerCall, self).__call__(*args, **kwargs)
    534 
    535   def get_concrete_function(self, *args, **kwargs):

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
    455 
    456     tracing_count = self._get_tracing_count()
--> 457     result = self._call(*args, **kwds)
    458     if tracing_count == self._get_tracing_count():
    459       self._call_counter.called_without_tracing()

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
    492       # In this case we have not created variables on the first call. So we can
    493       # run the first trace but we should fail if variables are created.
--> 494       results = self._stateful_fn(*args, **kwds)
    495       if self._created_variables:
    496         raise ValueError("Creating variables on a non-first call to a function"

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
   1820   def __call__(self, *args, **kwargs):
   1821     """Calls a graph function specialized to the inputs."""
-> 1822     graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
   1823     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   1824 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   2148         graph_function = self._function_cache.primary.get(cache_key, None)
   2149         if graph_function is None:
-> 2150           graph_function = self._create_graph_function(args, kwargs)
   2151           self._function_cache.primary[cache_key] = graph_function
   2152         return graph_function, args, kwargs

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   2039             arg_names=arg_names,
   2040             override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041             capture_by_value=self._capture_by_value),
   2042         self._function_attributes,
   2043         # Tell the ConcreteFunction to clean up its graph once it goes out of

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/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)
    913                                           converted_func)
    914 
--> 915       func_outputs = python_func(*func_args, **func_kwargs)
    916 
    917       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    356         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    357         # the function a weak reference to itself to avoid a reference cycle.
--> 358         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    359     weak_wrapped_fn = weakref.ref(wrapped_fn)
    360 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in wrapper(*args, **kwargs)
    513         layer, inputs=inputs, build_graph=False, training=training,
    514         saving=True):
--> 515       ret = method(*args, **kwargs)
    516     _restore_layer_losses(original_losses)
    517     return ret

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in wrap_with_training_arg(*args, **kwargs)
    109         training,
    110         lambda: replace_training_and_call(True),
--> 111         lambda: replace_training_and_call(False))
    112 
    113   # Create arg spec for decorated function. If 'training' is not defined in the

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/tf_utils.py in smart_cond(pred, true_fn, false_fn, name)
     57         pred, true_fn=true_fn, false_fn=false_fn, name=name)
     58   return smart_module.smart_cond(
---> 59       pred, true_fn=true_fn, false_fn=false_fn, name=name)
     60 
     61 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/framework/smart_cond.py in smart_cond(pred, true_fn, false_fn, name)
     54       return true_fn()
     55     else:
---> 56       return false_fn()
     57   else:
     58     return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn,

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in <lambda>()
    109         training,
    110         lambda: replace_training_and_call(True),
--> 111         lambda: replace_training_and_call(False))
    112 
    113   # Create arg spec for decorated function. If 'training' is not defined in the

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in replace_training_and_call(training)
    104     def replace_training_and_call(training):
    105       set_training_arg(training, training_arg_index, args, kwargs)
--> 106       return wrapped_call(*args, **kwargs)
    107 
    108     return tf_utils.smart_cond(

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in call_and_return_conditional_losses(inputs, *args, **kwargs)
    555   layer_call = _get_layer_call_method(layer)
    556   def call_and_return_conditional_losses(inputs, *args, **kwargs):
--> 557     return layer_call(inputs, *args, **kwargs), layer.get_losses_for(inputs)
    558   return _create_call_fn_decorator(layer, call_and_return_conditional_losses)
    559 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in get_losses_for(self, inputs)
   1382     losses = [l for l in self.losses if not l._unconditional_loss]
   1383     inputs = nest.flatten(inputs)
-> 1384     reachable = tf_utils.get_reachable_from_inputs(inputs, losses)
   1385     return [l for l in losses if l in reachable]
   1386 

/app/AI_RD/conda/envs/cont_tag_sup/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/tf_utils.py in get_reachable_from_inputs(inputs, targets)
    132       outputs = x.consumers()
    133     else:
--> 134       raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x))
    135 
    136     for y in outputs:

TypeError: Expected Operation, Variable, or Tensor, got None
**Describe the expected behavior**

**Code to reproduce the issue**

`import tensorflow as tf`
`import pandas as pd`

`from sklearn.model_selection import train_test_split`

`import transformers`
`from transformers import AlbertConfig`
`from transformers import AlbertTokenizer`
`from transformers import TFAlbertForSequenceClassification`
`from transformers import glue_convert_examples_to_features`

`data_df = pd.read_excel("../input/test.xlsx")`
`model_dir = '../input/albert_xxlarge_v2/'`
`EPOCHS = 3`
`MAX_SEQ_LENGTH = 256`
`label_list = [0,1]`

`config = AlbertConfig.from_pretrained('albert-xxlarge-v2')`
`tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2', cache_dir=model_dir)`
`model = TFAlbertForSequenceClassification.from_pretrained('albert-xxlarge-v2', ``cache_dir=model_dir, config=config)`

`train_df, test_df = train_test_split(data_df[['id','text1', 'text2', 'LABEL']], 
                                                    random_state=42, shuffle=True, 
                                                    test_size=0.20, stratify=data_df['LABEL'])`

`train_InputExamples = train_df.apply(lambda x: InputExample(guid=x['id'],
                                                                           text_a=x['text1'],
                                                                           text_b=x['text2'],
                                                                           label=x['LABEL']), axis=1)`

`train_dataset = glue_convert_examples_to_features(examples=train_InputExamples, tokenizer=tokenizer,
                                                  max_length=MAX_SEQ_LENGTH,
                                                  label_list = label_list, output_mode="classification")`

`optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08)`
`loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)`
`metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')`

`input_ids_train = []`
`attention_mask_train = []`
`token_type_ids_train = []`
`output_label_train = []`
`for f in train_dataset:`
    `input_ids_train.append(f.input_ids)`
    `attention_mask_train.append(f.attention_mask)`
    `token_type_ids_train.append(f.token_type_ids)`
    `output_label_train.append(f.label)`
`model.compile(optimizer=optimizer, loss=loss, metrics=[metric])`

`input_ids_train = np.array(input_ids_train)`
`attention_mask_train = np.array(attention_mask_train)`
`token_type_ids_train = np.array(token_type_ids_train)`
`output_label_train = np.array(output_label_train)`

`model.fit([input_ids_train,attention_mask_train, token_type_ids_train], y=output_label_train,
          epochs = EPOCHS, batch_size=4)`

`model.save('../output/my_model')`

The training of the model is successful, but getting errors only while saving the model<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
