NuSVC argument `class_weight` is not used

### Describe the bug

Like `SVC`, the class `NuSVC` takes argument `class_weight`. However, it looks like this argument is not used. After a quick look at the libsvm C code within sklearn as well as [libsvm's original documentation](https://www.csie.ntu.edu.tw/~cjlin/libsvm/), this seems to be expected: "`wi` set the parameter C of class i to weight*C, for C-SVC". I suggest that this argument should be removed from `NuSVC`'s constructor and from the documentation.

### Steps/Code to Reproduce

```python
from sklearn.svm import SVC, NuSVC

X = [[1., 2, 3], [0, 5, 2]]
y = [-1, 1]

NuSVC(verbose=True).fit(X, y).dual_coef_

optimization finished, #iter = 0
C = 2.587063
obj = 1.293532, rho = 0.000000
nSV = 2, nBSV = 0
Total nSV = 2
Out: [LibSVM]array([[-1.29353162,  1.29353162]])

SVC(C=2.587063, verbose=True).fit(X, y).dual_coef_

optimization finished, #iter = 1
obj = -1.293532, rho = 0.000000
nSV = 2, nBSV = 0
Total nSV = 2
Out: [LibSVM]array([[-1.29353162,  1.29353162]])

NuSVC(class_weight={-1:1.5, 1:.2}, verbose=True).fit(X, y).dual_coef_

optimization finished, #iter = 0
C = 2.587063
obj = 1.293532, rho = 0.000000
nSV = 2, nBSV = 0
Total nSV = 2
Out: [LibSVM]array([[-1.29353162,  1.29353162]])

SVC(C=2.587063, class_weight={-1:1.5, 1:.2}, verbose=True).fit(X, y).dual_coef_

optimization finished, #iter = 1
obj = -0.827860, rho = -0.600000
nSV = 2, nBSV = 1
Total nSV = 2
Out: [LibSVM]array([[-0.5174126,  0.5174126]])


NuSVC(class_weight={-1:0, 1:0}).fit(X, y).dual_coef_
Out: array([[-1.29353162,  1.29353162]])

SVC(class_weight={-1:0, 1:0}).fit(X, y).dual_coef_
Out: array([], shape=(1, 0), dtype=float64)
```


### Expected Results

As in the case of no `class_weight`, `NuSVM` should give the same `dual_coef_` as an `SVC` with the same `C`.
Also `class_weight={-1:0, 1:0}` should give the "empty" result.

### Actual Results

In all cases above `NuSVM` with class weight behaves exactly as when no weights are given.

### Versions

```shell
System:
    python: 3.9.16 | packaged by conda-forge | (main, Feb  1 2023, 21:39:03)  [GCC 11.3.0]
executable: .../bin/python3.9
   machine: Linux-6.8.0-48-generic-x86_64-with-glibc2.39

Python dependencies:
      sklearn: 1.5.2
          pip: 23.0.1
   setuptools: 67.6.0
        numpy: 2.0.2
        scipy: 1.13.1
       Cython: None
       pandas: None
   matplotlib: 3.9.2
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: blis
    num_threads: 1
         prefix: libblis
       filepath: .../lib/libblis.so.4.0.0
        version: 0.9.0
threading_layer: pthreads
   architecture: skx

       user_api: openmp
   internal_api: openmp
    num_threads: 8
         prefix: libgomp
       filepath: .../lib/libgomp.so.1.0.0
        version: None
```

COMMENT:
If sample weights work correctly, then I think we can indeed implement what is suggested in https://github.com/scikit-learn/scikit-learn/issues/30332#issuecomment-2504801670.

The problem is that, at this time, we cannot fully trust the implementation of `sample_weight` for Support Vector Machines in scikit-learn: in particular, they fail the reweighting/repetition equivalence checks:

```
$ pytest -k "check_sample_weight_equivalence and SVC" sklearn/tests/test_common.py -v
...
sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sample_weight_equivalence_on_dense_data] XFAIL (sample_weight is not equivalent to removing/repeating samples.)             [ 16%]
sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sample_weight_equivalence_on_sparse_data] XFAIL (sample_weight is not equivalent to removing/repeating samples.)            [ 33%]
sklearn/tests/test_common.py::test_estimators[NuSVC()-check_sample_weight_equivalence_on_dense_data] XFAIL (sample_weight is not equivalent to removing/repeating samples.)                            [ 50%]
sklearn/tests/test_common.py::test_estimators[NuSVC()-check_sample_weight_equivalence_on_sparse_data] XFAIL (sample_weight is not equivalent to removing/repeating samples.)                           [ 66%]
sklearn/tests/test_common.py::test_estimators[SVC()-check_sample_weight_equivalence_on_dense_data] XFAIL (sample_weight is not equivalent to removing/repeating samples.)                              [ 83%]
sklearn/tests/test_common.py::test_estimators[SVC()-check_sample_weight_equivalence_on_sparse_data] XFAIL (sample_weight is not equivalent to removing/repeating samples.)                             [100%]
...
```

I am not sure what is the cause of this problem. Maybe this is because the formulation of the support vector machine objective function in terms of discrete numbers of support vectors is fundamentally at odd with the checks we do with repeated data points in `_check_sample_weight_equivalence`.

Labels: Bug, Needs Investigation