# Description

This PR updates the `NoiseModelWithAdditiveBias` to apply per-feature
bias sampling.

Previously, the model sampled a single scalar bias per episode and
applied it uniformly across all feature dimensions (i.e., axis 1 of a
(`num_env`, `feature_dim`) tensor). This PR changes the behavior to
instead sample a separate bias value for each feature dimension, making
the model more suitable for structured inputs such as positions,
velocities, or multi-DOF actions.

### Notes

Structured inputs typically contain semantically distinct components,
like [x, y, z] coordinates, where applying the same bias across all
components introduces unrealistic, fully correlated noise. Independent
per-dimension bias sampling leads to more realistic and robust policy
training, especially for sim-to-real transfer.

I’ve replaced the previous behavior with this new default, as I believe
the original implementation could be misleading and not well-suited for
many practical scenarios. However, if desired, I can make both behaviors
available via a configuration flag (e.g., `per_feature_bias=True`), to
retain backward compatibility.

Fixes #2759.

## Type of change

- Bug fix (non-breaking change which fixes an issue)
- New feature (non-breaking change which adds functionality)

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

---------
Co-authored-by: Kelly Guo <kellyg@nvidia.com>