code: def _get_covariance(self, x1, x2):
      k_ux1 = to_dense(self.base_kernel(x1, self.inducing_points))
      if torch.equal(x1, x2):
          covar = LowRankRootLinearOperator(
              k_ux1.matmul(self._inducing_inv_root)
          )
  
          # Diagonal correction for predictive posterior
          if (
              not self.training
              and settings.sgpr_diagonal_correction.on()
          ):
              correction = (
                  self.base_kernel(x1, x2, diag=True)
                  - covar.diagonal(dim1=-1, dim2=-2)
              ).clamp(0, math.inf)
              covar = LowRankRootAddedDiagLinearOperator(
                  covar, DiagLinearOperator(correction)
              )
      else:
          k_ux2 = to_dense(self.base_kernel(x2, self.inducing_points))
          covar = MatmulLinearOperator(
              k_ux1.matmul(self._inducing_inv_root),
              k_ux2.matmul(self._inducing_inv_root).transpose(-1, -2),
          )
  
      return covar
  
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 5 for calling the InducingPointKernel obj's underlying member method code with example usage: obj._get_covariance(x1, x2) and returns: covar