code: def __init__(
      self, train_x, train_y, likelihood, base_kernel, inducing_points
  ):
      super().__init__(train_x, train_y, likelihood)
      self.mean_module = ZeroMean()
      self.covar_module = InducingPointKernel(
          base_kernel, inducing_points, likelihood
      )
      self.prediction_strategy = self.covar_module.prediction_strategy
  
imports: import copy
  import unittest.mock
  import gpytorch
  import numpy
  import torch
  from gpytorch.constraints.constraints import Interval
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.kernels.inducing_point_kernel import InducingPointKernel
  from gpytorch.lazy import LazyEvaluatedKernelTensor, delazify
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means.zero_mean import ZeroMean
  from gpytorch.models.exact_gp import ExactGP
  from smac.model.gaussian_process.kernels._boing import FITCKernel, FITCMean
  from smac.model.utils import check_subspace_points
label_desc: Use image node_id 1 to create a new FITC object from inherited base classes: ExactGP with example: obj = FITC(train_x, train_y, likelihood, base_kernel, inducing_points)