Feature parity with Pytorch's BatchNormNd, code clean-up

This release updates ABN, InPlaceABN and InPlaceABNSync to feature parity with recent versions of Pytorch's BatchNormNd layers:

• Add a track_running_stats parameter to enable / disable computation of running statistics independently from the layer's training state
• Add a num_batches_tracked buffer, and allow passing momentum=None to support cumulative moving average for tracking running stats instead of exponential moving average
• As a side-effect, now support loading parameters from standard BatchNorm without work-arounds. Still, if the loaded parameters contain negative weight elements the output will differ compared to standard BatchNorm

Additional changes:

• Fix backward pass in eval mode: it was not properly accounting for the activation function
• Refactor code to follow more sensible formatting standards
• Add type annotations
• Improve docstrings
• Update installation instructions, pointing to the PyPI package