text: Title: NuSVC argument `class_weight` is not used
  Body: ### 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
  ```
  