add more type support

[yuzawa-san]

• add new types, add support for existing types
• try to provide relevant buffer views based on type sizes

[codecov]

Codecov Report

❌ Patch coverage is 97.36842% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 94.16%. Comparing base (a7ddbb1) to head (6268cab).
⚠️ Report is 6 commits behind head on master.

Files with missing lines	Patch %	Lines
...n/java/com/jyuzawa/onnxruntime/TensorInfoImpl.java	88.88%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@             Coverage Diff              @@
##             master     #421      +/-   ##
============================================
+ Coverage     94.15%   94.16%   +0.01%     
  Complexity      503      503              
============================================
  Files            38       38              
  Lines          1608     1611       +3     
  Branches        121      121              
============================================
+ Hits           1514     1517       +3     
  Misses           70       70              
  Partials         24       24              

Flag	Coverage Δ
java-25-macos-latest	93.85% <97.36%> (+0.01%)	⬆️
java-25-ubuntu-latest	93.35% <97.36%> (+0.01%)	⬆️
java-25-windows-latest	93.35% <97.36%> (+0.01%)	⬆️

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[nk2IsHere]

It's great! Thank you!

As per float16 being treated as float array in onnx java implementation instead: https://github.com/microsoft/onnxruntime/blob/e8b9b24a31ed791d8cdfc515b2b5dfe46db9d6a6/java/src/main/java/ai/onnxruntime/TensorInfo.java#L341-L344

From our experience we also used float array to represent float16 tensors. If it is to be used with short array as a backing, its would be good to run a small identity model on them to confirm this is what onnx allows to do.