When loading models with MXFP4 quantization (e.g., openai/gpt-oss-20b),
the transformers library uses Triton tensors to wrap the quantized weights.
These Triton tensors have a .data attribute containing the underlying
PyTorch tensor, but torch.is_tensor() returns False for them.
This caused a KeyError: 'mlp.down_proj' when trying to load such models,
as the try_add() function would fail the assertion check before adding
the down projection matrices.
The fix extracts the underlying PyTorch tensor via the .data attribute
when encountering Triton tensors, allowing heretic to work with MXFP4
quantized models while maintaining full compatibility with standard models.
Tested with openai/gpt-oss-20b on PyTorch 2.9.1+cu130, transformers 4.57.1,
triton 3.5.1, and kernels 0.11.0.
