Implement Hadamard Gaussian noise

[TobyBoyne]

Addresses #877, #2416. Implements noise that is homoskedastic in inputs, but task-dependent.

Existing multi-task noises (https://docs.gpytorch.ai/en/latest/likelihoods.html#multi-dimensional-likelihoods) only work where all tasks are observed for each input. In the 'Hadamard' setting, where each input corresponds to exactly one task, there is no existing implementation that supports a different noise for each task.

See the updated Hadamard Multitask GP Regression for usage.

Unit tests, documentation, type hinting will all also be provided soon!

[TobyBoyne]

Two areas that likely need improving:

• The implementation of _shaped_noise_covar probably needs some work. I'm not sure if there is some linear_operator magic to make this neater?
• The syntax can be a little questionable. For example, I don't like the fact that the task indexes need to be in a list in mll(output, full_train_y, [full_train_i]). It is also not obvious that the implementation requires the task indexes to be passed as the last argument to the model, since they are accessed by params[0][-1]. At least some type checking is required here?

Also, I should credit @jpfolch for help with this implementation!