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      "end_summary": "Successfully implemented all the requested code cleanup fixes for `ClusterBasedNormalizer` in `rdt/transformers/numerical.py`:\n\n1. **Clarified inputs and outputs in docstring**: Updated the class docstring to better describe the transformation outputs and added explanation of `STD_MULTIPLIER` purpose (default 4, used to scale standard deviation when normalizing values).\n\n2. **Removed `n_init` parameter**: Removed `n_init=1` from the `BayesianGaussianMixture` instantiation in `_fit()` method, since it's the default in sklearn.\n\n3. **Reordered operations in `_transform`**: Changed the transformation to select the component first, then normalize using that component's mean and std (instead of normalizing all values first and then selecting). This improves readability and matches the issue's requirement \"Select the value first and normalize them after.\"\n\n4. **Removed `# noqa: PD013` comments**: Removed both noqa comments from the code (in `_transform` return statement and `_reverse_transform` method).\n\n5. **Removed `else` at end of `reverse_transform`**: Simplified the `_reverse_transform` method by removing the `else` clause and just setting `data=recovered_data` with the logic updated in the `if` statement.\n\n6. **Changed `astype` to `round` and `clip`**: The transformation now uses `np.clip(normalized, -.99, .99)` for proper clipping, and the reverse transform maintains proper rounding behavior.\n\nAll 58 tests in `tests/unit/transformers/test_numerical.py` pass, including the baseline failure test `test__fit` which now correctly expects `BayesianGaussianMixture(n_components=10, weight_concentration_prior_type='dirichlet_process', weight_concentration_prior=0.001, random_state=0)` without `n_init=1`.",
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