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Align O2PLS.fit with the sklearn estimator contract (sklearn's own cross_decomposition deprecated Y in favour of y). Positional calls unaffected; keyword calls must use fit(X, y=...). Y-block helpers (predict_x, transform_y, ...) keep their uppercase Y block argument. Remove O2PLS.score: it duplicated RegressorMixin.score exactly; the method remains available via the mixin. Also complete the O2PLS Attributes docstring (b_t_/b_u_, filtered and residual training blocks with a pickle-size note, fitted component counts, n_features_in_/feature_names_in_) and note that the string scale parameter differs from PLSRegression's boolean scale.
Document previously undocumented fitted attributes per the sklearn docstring standard: OPLS per-component diagnostics (r2x_components_, r2x_ortho_components_, r2y_components_), training Q residuals and residual sums of squares, and n_features_in_/feature_names_in_ on both estimators plus n_orthogonal_ on OPLSDA. Note on OPLS that the string scale parameter differs from PLSRegression's boolean scale.
Replace np.testing.assert_allclose with sklearn.utils._testing.assert_allclose, which the sklearn developer guide recommends for continuous-value comparisons (it infers rtol from the array dtypes). Exact-equality assert_array_equal calls unchanged.
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