Better iterators

[bengioe]

This PR refactors the SamplingIterator class into a DataSource class, where much of the existing functionality is decomposed into methods that can be combine with more clarity. Instead of interacting with SamplingIterator through a long list of sometimes unclear arguments, DataSource works by creating and combining iterators that perform more specific jobs.

Changes:

• SamplingIterator is now DataSource
• refactor around devices, device-needing code should now call get_worker_device(). This avoids passing around a device object (which, because of workers, can get confusing)
• adds an is_eval member to algorithms, and shifts the responsibility of determining random action probabilities to GFNAlgorithm
• adds SQLiteLogHook to generalize logging as a hook that's added to DataSource instances
• adds AvgRewardHook, a simple sampling hook to report average reward
• fixes minor MOO incongruities
• fixes Nested dataclasses do not reinitialize #123 , whereby nested dataclasses would be only initialized once
• fixes a timeout condition typo in MultiObjectiveStatsHook

TODO:

• address TODO items in this draft PR
• complete transition to get_worker_device and remove device objects being passed around

[julienroyd]

Taking a first look. Great refacto, I think it is much clearer now! Left a few comments (probably more to come).

[julienroyd]

Looks good to me! And much needed refacto, very nice result !

Small suggestion: could add just a few words about DataSource in implementation_notes.md, e.g.:

## Data sources

The data used for training GFlowNets can come from a variety of sources. `DataSource` implements these different use-cases as individual iterators that collectively assemble the training batches before passing it to the trainer. Some of these use-cases include:
- Generating new trajectories on-policy
- Sampling trajectories from passed policies from a replay buffer
- Sampling trajectories from a fixed, offline dataset 

`DataSource` also covers validation sets, including cases such as:
- Generating new trajectories (w.r.t a fixed dataset of conditioning goals)
- Evaluating the model's likelihood on trajectories from a fixed, offline dataset