Not Implemented Error 
# 🐛 Bug

## Information

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

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

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

## To reproduce
 I am trying to run the Bert model on a sequence of sentences. 
Steps to reproduce the behavior:
```
import tensorflow as tf
from transformers import BertTokenizer, TFBertModel
inputs = tf.keras.Input(shape=(50, 64), dtype='int32')
model = TFBertModel.from_pretrained('bert-base-uncased')
outputs = tf.keras.layers.TimeDistributed(model)(inputs)

```
I get a not implemented error, 
NotImplementedError                       Traceback (most recent call last)
<ipython-input-5-631f3cd2e8b2> in <module>
----> 1 outputs = tf.keras.layers.TimeDistributed(model)(inputs)

The same code works fine for 
```
inputs = tf.keras.Input(shape=(10, 128, 128, 3))
conv_2d_layer = tf.keras.layers.Conv2D(64, (3, 3))
outputs = tf.keras.layers.TimeDistributed(conv_2d_layer)(inputs)
outputs.shape
```


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## Expected behavior
I should be able to run the bert model on the sequence of sentences. 

<!-- A clear and concise description of what you would expect to happen. -->

## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.0.0
- Platform: Linux-5.0.0-1028-azure-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.8
- PyTorch version (GPU?): 1.5.1 (True)
- Tensorflow version (GPU?): 2.0.0 (True)
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no

This is likely due because you are using a wrong version of Tensorflow. Can you run `transformers-cli env`, as the template suggests, and place the result here?<|||||>Can you post the full stack trace? It's hard to debug this way.<|||||>@BramVanroy for your reference

```
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-2-db5a2030f0d4> in <module>
      3 inputs = tf.keras.Input(shape=(50, 64), dtype='int32')
      4 model = TFBertModel.from_pretrained('bert-base-uncased')
----> 5 outputs = tf.keras.layers.TimeDistributed(model)(inputs)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    840                     not base_layer_utils.is_in_eager_or_tf_function()):
    841                   with auto_control_deps.AutomaticControlDependencies() as acd:
--> 842                     outputs = call_fn(cast_inputs, *args, **kwargs)
    843                     # Wrap Tensors in `outputs` in `tf.identity` to avoid
    844                     # circular dependencies.

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/layers/wrappers.py in call(self, inputs, training, mask)
    254       y = self.layer(inputs, **kwargs)
    255       # Shape: (num_samples, timesteps, ...)
--> 256       output_shape = self.compute_output_shape(input_shape).as_list()
    257       output_shape = self._get_shape_tuple(
    258           (-1, input_length), y, 1, output_shape[2:])

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/layers/wrappers.py in compute_output_shape(self, input_shape)
    208     child_input_shape = tensor_shape.TensorShape([input_shape[0]] +
    209                                                  input_shape[2:])
--> 210     child_output_shape = self.layer.compute_output_shape(child_input_shape)
    211     if not isinstance(child_output_shape, tensor_shape.TensorShape):
    212       child_output_shape = tensor_shape.TensorShape(child_output_shape)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in compute_output_shape(self, input_shape)
    710   def compute_output_shape(self, input_shape):
    711     if not self._is_graph_network:
--> 712       return super(Network, self).compute_output_shape(input_shape)
    713 
    714     # Convert any shapes in tuple format to TensorShapes.

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py in compute_output_shape(self, input_shape)
    637                                       'layer (%s).' % self.__class__.__name__)
    638       return nest.map_structure(lambda t: t.shape, outputs)
--> 639     raise NotImplementedError
    640 
    641   @doc_controls.for_subclass_implementers

NotImplementedError: 
```<|||||>Could this be caused by the fact that TFBertModel returns a tuple (hidden states, pooled output)? In @4rshdeep's case, would we expect TimeDistributed to return a shape of (batch_size, 50, 768), or (batch_size, 50, 64, 768)?<|||||>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.
<|||||>I'm having the same error with using the data Streaming app on HuggingFace.