fix: differential abundance and aggregated posterior computations

[lordy5]

Fixes:

• Use the original anndata for aggregated posterior computation, not the subset of the anndata. All cells in a sample should be considered when computing the aggregated posterior (up to num_cells_posterior)
• Update the tests accordingly
• Correctly pass scales into torch distributions, not variances. get_latent_representation returns variances, not scales, while the aggregated posterior code previously expected scales, and so accidentally passed variances into torch distributions (Normal and Student's T expect scales instead)

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 88.53%. Comparing base (db0900c) to head (4391365).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #3789      +/-   ##
==========================================
+ Coverage   88.52%   88.53%   +0.01%     
==========================================
  Files         237      237              
  Lines       23823    23827       +4     
==========================================
+ Hits        21090    21096       +6     
+ Misses       2733     2731       -2     

Flag	Coverage Δ
integration	72.61% <100.00%> (+<0.01%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
src/scvi/model/base/_vaemixin.py	96.99% <100.00%> (+0.09%)	⬆️

... and 1 file with indirect coverage changes

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[ori-kron-wis]

@lordy5, please remove the redundant .vscode or .claude