[Python] Honor fixed object detection batch size

[bvolpato]

Related to #38782 and #39949.

Why

The object-detection example describes its inference batch size as fixed, but passes inference_batch_size to PytorchModelHandlerTensor. That handler does not consume this keyword. It reaches ModelHandler through **kwargs, where it is ignored, leaving RunInference to use adaptive BatchElements defaults.

This means all four Faster R-CNN benchmark variants have been measuring an unintended batching policy since they were added.

What changed

Pass the configured size through the supported min_batch_size and max_batch_size arguments. Setting both to the same value makes BatchElements use the requested fixed size, currently 8 in the benchmark configuration.

This is separate from #39949 because corrected batching may change runtime. The timeout change can remain draft until a Dataflow run shows whether 30 minutes is still insufficient.

Validation

• python -m py_compile sdks/python/apache_beam/examples/inference/pytorch_image_object_detection.py
• yapf==0.43.0 --diff on the changed file
• ruff==0.15.22 check --ignore I001,UP006 on the changed file
• git diff --check

The existing Python ML precommit covers PytorchModelHandlerTensor batching behavior.

[github-actions]

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R: @jrmccluskey for label python.

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[jrmccluskey]

LGTM, thanks!