code: def __init__(
      self,
      base_kernel: Kernel,
      inducing_points: Tensor,
      likelihood: Likelihood,
      active_dims: Optional[Tuple[int, ...]] = None,
  ):
      super(InducingPointKernel, self).__init__(active_dims=active_dims)
      self.base_kernel = base_kernel
      self.likelihood = likelihood
  
      if inducing_points.ndimension() == 1:
          inducing_points = inducing_points.unsqueeze(-1)
  
      self.register_parameter(
          name="inducing_points",
          parameter=torch.nn.Parameter(inducing_points),
      )
      self.register_added_loss_term("inducing_point_loss_term")
  
imports: import copy
  import math
  from typing import Optional, Tuple
  import torch
  from linear_operator import to_dense
  from linear_operator.operators import DiagLinearOperator, LowRankRootAddedDiagLinearOperator, LowRankRootLinearOperator, MatmulLinearOperator
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from torch import Tensor
  from ..None import settings
  from ..distributions import MultivariateNormal
  from ..likelihoods import Likelihood
  from ..mlls import InducingPointKernelAddedLossTerm
  from ..models import exact_prediction_strategies
  from .kernel import Kernel
label_desc: Use image node_id 1 to create a new InducingPointKernel object from inherited base classes: Kernel with example: obj = InducingPointKernel(base_kernel, inducing_points, likelihood, active_dims)