code: def __init__(
      self,
      logical_operator,
      kernel,
      degree,
      sv,
      nv,
      a,
      b,
      gamma,
      coef0,
      classes,
      device,
  ):
      super(SVC, self).__init__(logical_operator, classification=True)
      self.kernel = kernel
      self.degree = degree
      self.gamma = gamma
      self.regression = False
      sv = (
          sv.toarray()
          if isinstance(sv, scipy.sparse.csr_matrix)
          else sv
      )
      self.sv = torch.nn.Parameter(
          torch.from_numpy(sv).double(), requires_grad=False
      )
      self.sv_t = torch.nn.Parameter(
          torch.transpose(self.sv, 0, 1), requires_grad=False
      )
      self.sv_norm = torch.nn.Parameter(
          -self.gamma * (self.sv**2).sum(1).view(1, -1),
          requires_grad=False,
      )
      self.coef0 = coef0
      self.n_features = sv.shape[1]
      self.a = a
      self.b = torch.nn.Parameter(
          torch.from_numpy(b.reshape(1, -1)).double(),
          requires_grad=False,
      )
      self.start = [sum(nv[:i]) for i in range(len(nv))]
      self.end = [self.start[i] + nv[i] for i in range(len(nv))]
      self.len_nv = len(nv)
      true_classes, false_classes = zip(
          *[
              (i, j)
              for i in range(self.len_nv)
              for j in range(i + 1, self.len_nv)
          ]
      )
      self.true_classes = torch.nn.Parameter(
          torch.IntTensor([true_classes]), requires_grad=False
      )
      self.false_classes = torch.nn.Parameter(
          torch.IntTensor([false_classes]), requires_grad=False
      )
      self.classes = torch.nn.Parameter(
          torch.IntTensor(classes), requires_grad=False
      )
      self.perform_class_select = False
      if min(classes) != 0 or max(classes) != len(classes) - 1:
          self.perform_class_select = True
      self.n_classes = len(classes)
  
file_path: hummingbird/hummingbird/ml/operator_converters/_sv_implementations.py
imports: import numpy
  import scipy
  import torch
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 1 to create a new SVC object from inherited base classes: PhysicalOperator, torch.nn with example: obj = SVC(logical_operator, kernel, degree, sv, nv, a, b, gamma, coef0, classes, device)