Added object oriented approach for BST. Please review and let me know if any changes are required.

I'm using mxnet to do some work, but there is nothing when I search the mxnet trial and example.

There are (at least) two ways in which the reinterpret_cast is misused:

1. Used instead of static_cast:
   https://github.com/rapidsai/cudf/blob/f7fbc1160b17b969db2708e0cf6033d3db9fc1cf/cpp/src/io/avro/avro_gpu.cu#L95
   static cast should be used to cast from void*.
2. The use causes undefined behavior:
   https://github.com/rapidsai/cudf/blob/f7fbc1160b17b969db2708e0cf6033d3db9fc1cf/cpp/src/io

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

All dev environments should install faiss from conda. The 10.2 environment is currently missing it. It's also missing ucx-proc. Should follow the other env styles.

See: https://github.com/rapidsai/cuml/blob/branch-0.15/conda/environments/cuml_dev_cuda10.2.yml

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually