Fix RTN supported layer checking condition

[Kaihui-intel]

Type of Change

bug fix

Description

Issue: FalconLinear is not quantized.
root cause: falcon model 's linear is built-in by itself, not used torch.nn.linear, the issue depends on INC return model
solution: change supported layers name string to isinstance checking.

Expected Behavior & Potential Risk

the expected behavior that triggered by this PR

How has this PR been tested?

how to reproduce the test (including hardware information)

Dependency Change?

any library dependency introduced or removed

[github-actions]

⛈️ Required checks status: Has failure 🔴

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🟢 Code Scan Tests workflow

Check ID	Status	Error details
Code-Scan	success		✅
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Code-Scan (Pylint Code Scan Pylint)	success		✅

These checks are required after the changes to neural_compressor/adaptor/torch_utils/weight_only.py.

🟢 Model Tests workflow

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Model-Test	success		✅
Model-Test (Generate Report GenerateReport)	success		✅
Model-Test (Run ONNX Model resnet50-v1-12)	success		✅
Model-Test (Run PyTorch Model resnet18)	success		✅
Model-Test (Run PyTorch Model resnet18_fx)	success		✅
Model-Test (Run TensorFlow Model darknet19)	success		✅
Model-Test (Run TensorFlow Model inception_v1)	success		✅
Model-Test (Run TensorFlow Model resnet-101)	success		✅
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Model-Test (Run TensorFlow Model ssd_resnet50_v1)	success		✅

These checks are required after the changes to neural_compressor/adaptor/torch_utils/weight_only.py.

🟢 Unit Tests basic workflow

Check ID	Status	Error details
UT-Basic	success		✅
UT-Basic (Coverage Compare CollectDatafiles)	success		✅
UT-Basic (Unit Test FWKs adaptor Test FWKs adaptor)	success		✅
UT-Basic (Unit Test FWKs adaptor baseline Test FWKs adaptor baseline)	success		✅
UT-Basic (Unit Test ITEX Test ITEX)	success		✅
UT-Basic (Unit Test ITEX baseline Test ITEX baseline)	success		✅
UT-Basic (Unit Test Pruning Test PyTorch Pruning)	success		✅
UT-Basic (Unit Test Pruning Test TensorFlow Pruning)	success		✅
UT-Basic (Unit Test Pruning baseline Test PyTorch Pruning baseline)	success		✅
UT-Basic (Unit Test Pruning baseline Test TensorFlow Pruning baseline)	success		✅
UT-Basic (Unit Test TF newAPI Test TF newAPI)	success		✅
UT-Basic (Unit Test TF newAPI baseline Test TF newAPI baseline)	success		✅
UT-Basic (Unit Test User facing API Test User facing API)	success		✅
UT-Basic (Unit Test User facing API baseline Test User facing API baseline)	success		✅
UT-Basic (Unit Test other basic case Test other basic case)	success		✅
UT-Basic (Unit Test other cases baseline Test other cases baseline)	success		✅

UT-Basic coverage report

	Base coverage	PR coverage	Diff
Lines	86.971%	86.992%	0.021%
Branches	76.332%	76.332%	0.000%

These checks are required after the changes to neural_compressor/adaptor/torch_utils/weight_only.py.

🟢 Unit Tests basic no coverage workflow

Check ID	Status	Error details
UT-Basic-No-Coverage	success		✅
UT-Basic-No-Coverage (Unit Test FWKs adaptor Test FWKs adaptor)	success		✅
UT-Basic-No-Coverage (Unit Test Pruning Test PyTorch Pruning)	success		✅
UT-Basic-No-Coverage (Unit Test Pruning Test TensorFlow Pruning)	success		✅
UT-Basic-No-Coverage (Unit Test User facing API Test User facing API)	success		✅
UT-Basic-No-Coverage (Unit Test other basic case Test other basic case)	success		✅

These checks are required after the changes to neural_compressor/adaptor/torch_utils/weight_only.py.

🔴 Unit Tests ITREX workflow

Check ID	Status	Error details
UT-ITREX	failure	download	❌

These checks are required after the changes to neural_compressor/adaptor/torch_utils/weight_only.py.

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[Kaihui-intel]

3x will fix on another PR