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* feat(transforms): add 8 robotics-relevant image augmentations Add GaussianNoise, MotionBlur, JPEGCompression, GaussianPatchBrightness, RandomShadow, CoarseDropout, GammaCorrection, and PlanckianJitter. Each transform addresses a real-world failure mode not covered by the existing 6 defaults (sensor noise, motion blur, compression artifacts, uneven lighting, cast shadows, partial occlusion, exposure variation, color temperature shift). All transforms are pure PyTorch, follow the make_params/transform pattern, and integrate with ImageTransformConfig via a registry. * add augmentation showcase image for PR * update showcase with better sample frame * tune showcase to balanced augmentation intensity * tune showcase: softer shadow, dropout, jitter intensity * refactor(transforms): several updates * update image * chore(media): remove example * chore: add link to example --------- Co-authored-by: Yuxian LI <liyuxian1358@gmail.com>