BertForSequenceClassification is not optimum
In the documentation about TFBertModel it says:

```
hidden_states (tuple(tf.Tensor), optional, returned when config.output_hidden_states=True):
tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

```
Hidden-states of the model at the output of each layer plus the initial embedding outputs.

But then in the code of BertForSequenceClassification:

```
        outputs = self.bert(inputs, **kwargs)

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output, training=kwargs.get("training", False))
        logits = self.classifier(pooled_output)

        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here

        return outputs  # logits, (hidden_states), (attentions)
```
Wouldn't give better results using `pooled_output = outputs[0][:,0]` ?
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