Batch suggest in Omikuji backend

[osma]

This PR clarifies that backends only have to implement either one of _suggest and _suggest_batch, then implements batched suggest in the Omikuji backend. In practice, only the text vectorization is performed on the whole batch at once; the Omikuji implementation only supports a predict method for a single document at a time so it has to be done within a for loop.

There seems to be a small performance benefit. I tested this using annif eval the Finto AI yso-parabel-fi project/model, with the kirjaesittelyt2021/fin/test corpus. The evaluation results were unchanged, only the amount of time spent was slightly different. Memory usage remained pretty much the same.

With 1 job

	user time	wall time	max rss
before (master)	86.69	1:29.94	6322624
after (PR)	78.66	1:22.79	6336584

With 4 jobs

	user time	wall time	max rss
before (master)	121.33	1:22.33	6293804
after (PR)	96.55	1:18.78	6293640

Fixes #665

[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
0.0% Duplication

[codecov]

Codecov Report

Base: 99.56% // Head: 99.56% // Increases project coverage by +0.00% 🎉

Coverage data is based on head (f4b55cd) compared to base (a7e3b4b).
Patch coverage: 100.00% of modified lines in pull request are covered.

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #669   +/-   ##
=======================================
  Coverage   99.56%   99.56%           
=======================================
  Files          87       87           
  Lines        6143     6145    +2     
=======================================
+ Hits         6116     6118    +2     
  Misses         27       27           

Impacted Files	Coverage Δ
annif/backend/backend.py	100.00% <ø> (ø)
annif/backend/omikuji.py	97.53% <100.00%> (+0.09%)	⬆️

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[juhoinkinen]

LGTM