Added DynamicEmbedding RFC

[divyashreepathihalli]

Added DynamicEmbedding RFC

[Mr-Nineteen]

Dynamic embedding is a very important feature for us.

When training the sorting model that supports scenarios such as search, recommendation, and advertisement, we encountered the following problems:

1. For the feature selection of the sorting model in the e-commerce search and promotion scenario, the current industry mostly adopts the idea of large-scale discrete IDs. ID features (product IDs, user IDs, brand IDs, etc.) are large and sparse, and the native TF framework is not applicable.
2. The TensorFlow variable has a fixed size and cannot dynamically increase the ID without restarting training.

With this feature, the main reasons:

1. Support dynamic scale-out of dynamic embedding features at the TB level.