from Qtorch.Models.Qnn import Qnn 
from abc import ABC, abstractmethod

class QSVM(Qnn, ABC):
  def __init__(self, data, labels=None, test_size=0.2, random_state=None):
    super().__init__(data, labels, test_size, random_state)
    self.result.update({
      "pca_2d" : None,
      "pca_3d" : None
    })
  
  @abstractmethod
  def train_model(self, train_loader, test_loader, epochs):
    return super().train_model(train_loader, test_loader, epochs)