Separate interior state and boundary forcing to only predict state

[joeloskarsson]

Describe your changes

The goal of this PR is to establish a clear separation of the (predicted) state in the interior region and the boundary forcing coming from outside (and potentially overlapping with) the limited area. This PR performs these changes on the modeling side. That is from each batch is fetched from the dataset class and onward. Including how the tensors are propagated through the model, loss calculation, evaluation and plotting. This is complemented with #90 for handling the data-loading side of things, where the boundary forcing comes from a separate Datastore.

After this change:

• The model will only predict outputs within the limited area considered
• Plots will only include points within the limited area (this could be expanded in a future PR to plot also boundary fields, but would require a mapping between state and boundary forcing dimensions to plot together)
• Graphs will have to be created using a boundary mask as in Add a decoding mask option to only include subset of grid nodes in m2g weather-model-graphs#34 to make sure that the g2m-component only maps to the interior nodes.

Dependencies:

This introduces a dependency to https://github.com/mllam/weather-model-graphs. In particular, this dependency should be adjusted before merging to require a version after mllam/weather-model-graphs#34 has been merged.

Issue Link

No issue specific to the separation of interior state. This relates to the overall rework of Reading Training Data, but would be good to put as separate point on roadmap.

This includes graph-creation with wmg (#83).

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

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