code: def __init__(
      self,
      logical_operator,
      kernel,
      degree,
      sv,
      scaled_alphas,
      gamma,
      coef0,
      k_fit_rows,
      k_fit_all,
      device,
  ):
      super(KernelPCA, self).__init__(logical_operator)
      self.transformer = True
      self.kernel = kernel
      self.degree = degree
      self.n_samples = sv.shape[0]
      self.sv = torch.nn.Parameter(
          torch.from_numpy(sv).float(), requires_grad=False
      )
      self.n_features = sv.shape[1]
      self.k_fit_rows = torch.nn.Parameter(
          torch.from_numpy(k_fit_rows).float(), requires_grad=False
      )
      self.k_fit_all = k_fit_all
      if gamma is None:
          gamma = 1.0 / self.n_features
      self.gamma = gamma
      self.coef0 = coef0
      self.scaled_alphas = torch.nn.Parameter(
          torch.from_numpy(scaled_alphas).float(), requires_grad=False
      )
  
file_path: hummingbird/hummingbird/ml/operator_converters/_decomposition_implementations.py
imports: import torch
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 1 to create a new KernelPCA object from inherited base classes: PhysicalOperator, torch.nn with example: obj = KernelPCA(logical_operator, kernel, degree, sv, scaled_alphas, gamma, coef0, k_fit_rows, k_fit_all, device)