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Remove is_v2_0

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A default of 1 all the times is bad for TPU users which end up not using the device effectively.
A larger default all the times is bad for GPU users.

So compromise and make this dependent on the devices available.

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Update nlp.modeling.layers.ReZeroTransformer, to have the same interface with nlp.modeling.layers.Transformer

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Add network and BERT model to perform per-token classification (e.g., for named entity recognition tasks).

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Profiler has been integrated with TensorBoard callback and released in TF 2.2

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The intention of this change is to reduce the code complexity within Keras class, especially for Network, which currently contains logic for both subclass Model and functional Model.

After this change, the subclass model and functional model become individual class and become self contained.

1. Model is now the base class for subclass model. It doesn't contains network structure management, and the topology will be created within __init__ and __call__, which is for user to implement. It also contains compile/fit/eval/predict, which is the basic functionality for model training.

2. Functional is created based on existing Network class. It extends the Model, which allows it leverage compile/fit/eval/predict. In addition, it also take input/output as init parameter and manage the network topology.

3. Sequential model is now a subclass of Functional, since it will use Functional's method to manage it topology (layer stacking).

Model(input, output) will create a Functional under the hood, and behave the same way as before.

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Add an interface in nlp.modeling.networks.encoder_scaffold.EncoderScaffold and nlp.modeling.networks.transformer_encoder.TransformerEncoder, to get the pooler Dense layer reference.

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