Data time subsetting

[SarahAlidoost]

closes #62
closes #42

🔴 this branch should be merged after #63

In this PR:

• monthly data are integrated to batch dimension
• the bias/scale (per-month parameters) are removed to make the model more flexible. We are using residuals training, so the mean seasonal signal is already removed. We are also injecting strong seasonal structure inside the network via cyclic time embedings, temporal attention over days, cross-month mixing in the aggregator.
  So the model is already learning a seasonal climatology and modulation, not relying on a fixed per-month correction.

[meiertgrootes]

See comment in PR #54 on replacing month positional encoding with a month-of-year feature in cyclical time embedding

[SarahAlidoost]

@meiertgrootes and @rogerkuou Hi, this PR is ready for review. Thanks! After merging #63 , I can update the notebook in this PR.

[rogerkuou]

Hi @SarahAlidoost, thanks for the nice implementation.

I just have two small comments on an inline doc, and use of pytest fixture. Please feel free to merge after fixing them.