Major Features and Improvements

Add params as an optional third argument to the embedding_fn argument of
nsl.estimator.add_graph_regularization. This is similar to the params
argument of an Estimator's model_fn, which allows users to pass arbitrary
states through. Adding this as an argument to embedding_fn will allow
users to access that state in the implementation of embedding_fn.
Both nsl.keras.AdversarialRegularization and
nsl.keras.GraphRegularization now support the save method which will
save the base model.
nsl.keras.AdversarialRegularization now supports a tf.keras.Sequential
base model with a tf.keras.layers.DenseFeatures layer.
nsl.configs.AdvNeighborConfig has a new field random_init. If set to
True, a random perturbation will be performed before FGSM/PGD steps.
nsl.lib.gen_adv_neighbor now has a new parameter use_while_loop. If set
to True, the PGD steps are done in a tf.while_loop which is potentially
more memory efficient but has some restrictions.
New library functions:
- nsl.lib.random_in_norm_ball for generating random tensors in a norm
  ball.
- nsl.lib.project_to_ball for projecting tensors onto a norm ball.

Bug Fixes and Other Changes

Dropped Python 2 support (which was deprecated 2+ years ago).
nsl.keras.AdversarialRegularization and nsl.lib.gen_adv_neighbor will
not attempt to calculate gradients for tensors with a non-differentiable
dtype. This doesn’t change the functionality, but only suppresses excess
warnings.
Both estimator/adversarial_regularization.py and
estimator/graph_regularization.py explicitly import estimator from
tensorflow as a separate import instead of accessing it via tf.estimator
and depend on the tensorflow estimator target.
The new top-level workshops directory contains presentation materials from
tutorials we organized on NSL at KDD 2020, WSDM 2021, and WebConf 2021.
The new usage.md page describes featured usage of NSL, external talks,
blog posts, media coverage, and more.
End-to-end examples under the examples directory:
- New examples about graph neural network modules with graph-regularizer
  and graph convolution.
- New README file providing an overview of the examples.
New tutorial examples under the examples/notebooks directory:
- Graph regularization for image classification using synthesized graphs
- Adversarial Learning: Building Robust Image Classifiers
- Saving and loading NSL models

This release contains contributions from many people at Google Research and from
TF community members: @angela-wang1 , @dipanjanS, @joshchang1112, @SamuelMarks,
@sayakpaul, @wangbingnan136, @zoeyz101