Stop getitem quantizer propagation on numel change

[tej-1916]

Summary

Fixes TorchFX quantizer propagation through operator.getitem when the selected tensor has a different number of elements than the source tensor.

Root cause

TorchFX graph conversion always classified operator.getitem as PTGatherMetatype, and gather is quantization-agnostic. As a result, activation quantizers could propagate backward through split/getitem boundaries and land on the producer tensor even when downstream weighted ops consumed smaller selected tensors.

Changes

• Add a TorchFX metadata check that proves whether operator.getitem preserves tensor numel.
• Mark unsafe or unproven getitems with an internal NNCF node propagation-barrier attribute while preserving their public PTGatherMetatype.
• Route marked getitems through existing autogenerated ignored-node handling and exclude them as activation post-hook insertion points.
• Add focused TorchFX regression tests for different-numel and same-numel getitem behavior.

Validation

• python -m pytest tests/torch/fx/test_quantizer_config.py -k "getitem_different_numel or getitem_same_numel" -vv
• python -m pytest tests/torch/fx/test_quantizer_config.py -q
• python -m pytest tests/torch/fx/test_models.py -k "yolo26 or yolo11n or split or getitem" -q
• python -m ruff format --check src/nncf/common/graph/graph.py src/nncf/experimental/torch/fx/nncf_graph_builder.py src/nncf/quantization/algorithms/min_max/algorithm.py tests/torch/fx/test_quantizer_config.py
• python -m ruff check src/nncf/common/graph/graph.py src/nncf/experimental/torch/fx/nncf_graph_builder.py src/nncf/quantization/algorithms/min_max/algorithm.py tests/torch/fx/test_quantizer_config.py
• git diff --check

[tej-1916]

The implementation and focused regression tests are ready for review. CI workflows currently show action required; could a maintainer please approve the workflow runs when convenient? Thank you.