code: def forward(self, x1, x2, diag=False, **kwargs):
      covar = self._get_covariance(x1, x2)
  
      if self.training:
          if not torch.equal(x1, x2):
              raise RuntimeError("x1 should equal x2 in training mode")
          zero_mean = torch.zeros_like(x1.select(-1, 0))
          new_added_loss_term = InducingPointKernelAddedLossTerm(
              MultivariateNormal(zero_mean, self._covar_diag(x1)),
              MultivariateNormal(zero_mean, covar),
              self.likelihood,
          )
          self.update_added_loss_term(
              "inducing_point_loss_term", new_added_loss_term
          )
  
      if diag:
          return covar.diagonal(dim1=-1, dim2=-2)
      else:
          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 7 for calling the InducingPointKernel obj's underlying member method code with example usage: obj.forward(x1, x2, diag) and returns: covar, covar