docstr: Fit the Nystroem approximation and Sklearn `SGDOneClassSVM` SVM model.
  
  Parameters
  ----------
  x_ref
      Training data.
  tol
      The decrease in loss required over the previous ``n_iter_no_change`` iterations in order to
      continue optimizing.
  max_iter
      The maximum number of optimization steps.
  verbose
      Verbosity level during training. ``0`` is silent, ``1`` a progress bar.
  
  Returns
  -------
  Dictionary with fit results. The dictionary contains the following keys:
      - converged: `bool` indicating whether training converged.
      - n_iter: number of iterations performed.
docstr_tok: ["Fit","the","Nystroem","approximation","and","Sklearn","`","SGDOneClassSVM","`","SVM","model",".","Parameters","--","--","--","--","--","x_ref","Training","data",".","tol","The","decrease","in","loss","required","over","the","previous","``","n_iter_no_change","``","iterations","in","order","to","continue","optimizing",".","max_iter","The","maximum","number","of","optimization","steps",".","verbose","Verbosity","level","during","training",".","``","0","``","is","silent",",","``","1","``","a","progress","bar",".","Returns","--","--","--","-","Dictionary","with","fit","results",".","The","dictionary","contains","the","following","keys",":","-","converged",":","`","bool","`","indicating","whether","training","converged",".","-","n_iter",":","number","of","iterations","performed","."]
code: def fit(  # type: ignore[override]
      self,
      x_ref: torch.Tensor,
      tol: float = 1e-6,
      max_iter: int = 1000,
      verbose: int = 0,
  ) -> Dict:
      """Fit the Nystroem approximation and Sklearn `SGDOneClassSVM` SVM model.
  
      Parameters
      ----------
      x_ref
          Training data.
      tol
          The decrease in loss required over the previous ``n_iter_no_change`` iterations in order to
          continue optimizing.
      max_iter
          The maximum number of optimization steps.
      verbose
          Verbosity level during training. ``0`` is silent, ``1`` a progress bar.
  
      Returns
      -------
      Dictionary with fit results. The dictionary contains the following keys:
          - converged: `bool` indicating whether training converged.
          - n_iter: number of iterations performed.
      """
      x_nys = self.nystroem.fit(x_ref).transform(x_ref)
      self.svm = SGDOneClassSVM(
          tol=tol, max_iter=max_iter, verbose=verbose, nu=self.nu
      )
      x_nys = x_nys.cpu().numpy()
      self.svm = self.svm.fit(x_nys)
      self._set_fitted()
      return {
          "converged": self.svm.n_iter_ < max_iter,
          "n_iter": self.svm.n_iter_,
      }
  
imports: import warnings
  from typing import Callable, Dict, Optional, Tuple
  import numpy
  import torch
  from sklearn.linear_model import SGDOneClassSVM
  from sklearn.utils.extmath import safe_sparse_dot
  from tqdm import tqdm
  from typing_extensions import Self
  from alibi_detect.od.pytorch.base import TorchOutlierDetector
  from alibi_detect.utils.pytorch.losses import hinge_loss
  from alibi_detect.utils.pytorch.kernels import GaussianRBF
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 2 for calling the SgdSVMTorch obj's underlying member method code with example usage: obj.fit(x_ref, tol, max_iter, verbose) and returns: dict