code: def _covar_diag(self, inputs):
      if inputs.ndimension() == 1:
          inputs = inputs.unsqueeze(1)
  
      # Get diagonal of covar
      covar_diag = to_dense(self.base_kernel(inputs, diag=True))
      return DiagLinearOperator(covar_diag)
  
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 6 for calling the InducingPointKernel obj's underlying member method code with example usage: obj._covar_diag(inputs) and returns: DiagLinearOperator