cc @ezyang @anjali411 @dylanbespalko

The documentation in DJL was originally written with the expectation that users are reasonably familiar with deep learning. So, it does not go out of the way to define and explain some of the key concepts. To help users who are newer to deep learning, we created a [documentation convention](https://github.com/awslabs/djl/blob/master/docs/development/development_guideline.md#documentation-conventio

The docs generated for #431 at https://mratsim.github.io/Arraymancer/pca.html have broken formatting:

• Upstream bug on the type:
  
• Random bolding:
  and
  ![image](https:/

Currently if a user wants to determine if a particular operation is supported on a device from the documentation (e.g., in the default.qubit plugin), they will find a bunch of gates/operations/observables listed, but under different names than found in PennyLane. This may be confusing to users. We should state very clearly

Our neuron models currently use and return states with neuron parameters. We could consider either using the PyTorch register buffers: https://pytorch.org/docs/stable/nn.html#torch.nn.Module.register_buffer

Should simply call numpy.any on the tensor's underlying numpy array.

This is important because it helps to fix and avoid potential bugs, avoids the creation of unnecessary copies, perform some speed-ups and normalizes the code.