[Tests] Add tests for "quantization_w4a4_fp4" examples

[dbarbuzzi]

SUMMARY:
Adds tests for the example scripts in the examples/quantization_w4a4_fp4 folder.

TEST PLAN:
Internal examples test run with just the new tests: https://github.com/neuralmagic/llm-compressor-testing/actions/runs/16831501387/job/47709542536

The failure appears to be a legitimate issue, not a test issue, and has been reported.

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @dbarbuzzi, 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!

I've implemented new tests to ensure the example scripts located in the examples/quantization_w4a4_fp4 directory are functioning as expected. These tests validate that the scripts run without errors and produce output in the correct format, thereby improving the reliability and maintainability of these examples.

Highlights

• New Test File: I've added a new test file, tests/examples/test_quantization_w4a4_fp4.py, specifically designed to validate the example scripts in the examples/quantization_w4a4_fp4 directory.
• Parameterized Example Testing: The new tests utilize pytest.mark.parametrize to run llama3_example.py, llama4_example.py, and qwen_30b_a3b.py examples.
• Output Validation: Each test verifies that the example script executes successfully, generates the expected *-NVFP4 output directory, and that the config.json within this directory correctly specifies the nvfp4-pack-quantized format.
• GPU Requirement: These tests are configured to run only when at least one GPU is available, ensuring they are executed in the appropriate environment.

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[gemini-code-assist]

Code Review

This pull request adds tests for the quantization_w4a4_fp4 examples. The new test file tests/examples/test_quantization_w4a4_fp4.py correctly parameterizes tests for three example scripts and verifies their output. My review includes a couple of suggestions to improve the robustness and clarity of the test code, such as making a file search more specific and simplifying the use of pytest.mark.parametrize.