Allow inference args to be passed in for most cases

[damccorm]

Most model handlers try to accept inference args, but the base ModelHandler implementation doesn't allow it unless you override validate_inference_args (which most model handlers don't). This removes this limitation and allows passing inference args by default.

Fixes #37093

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[gemini-code-assist]

Summary of Changes

Hello @damccorm, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refactors the handling of inference_args within Beam's ML inference ModelHandler base class. It shifts the default behavior to allow inference_args to be passed, simplifying implementations for model handlers that support them. Concurrently, it enables specific handlers to explicitly disallow or utilize these arguments as needed, leading to more flexible and consistent argument management across different ML frameworks.

Highlights

• Default Inference Args Handling: The ModelHandler base class in sdks/python/apache_beam/ml/inference/base.py now permits inference_args by default, removing the previous ValueError that was raised if they were present.
• Streamlined Handler Implementations: Several model handlers, including those for PyTorch, TensorFlow, and Vertex AI, have removed their explicit validate_inference_args overrides, as the new base class behavior makes them redundant.
• Explicit Arg Usage in Scikit-learn: The Scikit-learn inference handler (sdks/python/apache_beam/ml/inference/sklearn_inference.py) has been updated to correctly pass inference_args to the model.predict method.
• Specific Arg Restriction for TensorRT: The TensorRT inference handler (sdks/python/apache_beam/ml/inference/tensorrt_inference.py) now explicitly disallows inference_args by re-introducing a ValueError in its validate_inference_args method, indicating non-support for this specific framework.

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[codecov]

Codecov Report

❌ Patch coverage is 50.00000% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 55.13%. Comparing base (576a802) to head (d71ddb1).

Files with missing lines	Patch %	Lines
...hon/apache_beam/ml/inference/tensorrt_inference.py	0.00%	3 Missing ⚠️

Additional details and impacted files

@@              Coverage Diff              @@
##             master   #37094       +/-   ##
=============================================
+ Coverage     36.31%   55.13%   +18.81%     
  Complexity     1676     1676               
=============================================
  Files          1067     1067               
  Lines        167122   167116        -6     
  Branches       1208     1208               
=============================================
+ Hits          60695    92135    +31440     
+ Misses       104247    72801    -31446     
  Partials       2180     2180               

Flag	Coverage Δ
python	80.77% <50.00%> (+40.15%)	⬆️

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[github-actions]

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R: @jrmccluskey for label python.

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[jrmccluskey]

LGTM