Pytigergraph

Release of pyTigerGraph version 1.1.

Added:
* TensorFlow support for homogeneous GNNs via the Spektral library.
* Heterogeneous Graph Dataloading support for DGL.
* Support of lists of strings in dataloaders.

Changed:
* Fixed KeyError when creating a data loader on a graph where PrimaryIdAsAttribute is False.
* Error catch if Kafka dataloader doesn't run in async mode.
* Refresh schema during dataloader instantiation and featurizer attribute addition.
* Reduce connection instantiation time.
* Reinstall query if it is disabled.
* Confirm Kafka topic is created before subscription.
* More efficient use of Kafka resources.
* Allow multiple consumers on the same data.
* Improved deprecation warnings.

Release of pyTigerGraph version 1.0, in conjunction with version 1.0 of the link:https://docs.tigergraph.com/ml-workbench/current/overview/[TigerGraph Machine Learning Workbench].

Added:
* Kafka authentication support for ML Workbench enterprise users.
* Custom query support for Featurizer, allowing developers to generate their own graph-based features as well as use our link:https://docs.tigergraph.com/graph-ml/current/intro/[built-in Graph Data Science algorithms].

Changed:
* Additional testing of GDS functionality
* More demos and tutorials for TigerGraph ML Workbench, found link:https://github.com/TigerGraph-DevLabs/mlworkbench-docs[here].
* Various bug fixes.

We are excited to announce the pyTigerGraph v0.9 release! This release adds many new features for graph machine learning and graph data science, a refactoring of core code, and more robust testing. Additionally, we have officially “graduated” it to an official TigerGraph product. This means brand-new documentation, a new GitHub repository, and future feature enhancements. While becoming an official product, we are committed to keeping pyTigerGraph true to its roots as an open-source project. Check out the contributing page and GitHub issues if you want to help with pyTigerGraph’s development.
Changed
* Feature: Include Graph Data Science Capability
- Many new capabilities added for graph data science and graph machine learning. Highlights include data loaders for training Graph Neural Networks in DGL and PyTorch Geometric, a "featurizer" to generate graph-based features for machine learning, and utilities to support those activities.

* Documentation: We have moved the documentation to [the official TigerGraph Documentation site](https://docs.tigergraph.com/pytigergraph/current/intro/) and updated many of the contents with type hints and more descriptive parameter explanations.

* Testing: There is now well-defined testing for every function in the package. A more defined testing framework is coming soon.

* Code Structure: A major refactor of the codebase was performed. No breaking changes were made to accomplish this.

Changed
* Fix : added safeChar method to fix URL encoding

Changed
* Fix : removed the localhost to 127.0.0.1 translation

Changed
* Fix : SSL issue with Rest++ for self-signed certs
* Fix : Updates for pyTigerDriver bounding
* Feature : added the checks to debug
* Fix : added USE GRAPH cookie