code: def forward(
      self,
      x: Tensor,
      inducing_points: Tensor,
      inducing_values: Tensor,
      variational_inducing_covar: Optional[LinearOperator] = None,
      *params,
      **kwargs,
  ) -> MultivariateNormal:
      if x.ndimension() == 1:
          x = x.unsqueeze(-1)
      elif x.ndimension() != 2:
          raise RuntimeError(
              "AdditiveGridInterpolationVariationalStrategy expects a 2d tensor."
          )
  
      num_data, num_dim = x.size()
      if num_dim != self.num_dim:
          raise RuntimeError(
              "The number of dims should match the number specified."
          )
  
      output = super().forward(
          x,
          inducing_points,
          inducing_values,
          variational_inducing_covar,
      )
      if self.sum_output:
          if variational_inducing_covar is not None:
              mean = output.mean.sum(0)
              covar = output.lazy_covariance_matrix.sum(-3)
              return MultivariateNormal(mean, covar)
          else:
              return Delta(output.mean.sum(0))
      else:
          return output
  
file_path: gpytorch/gpytorch/variational/additive_grid_interpolation_variational_strategy.py
imports: from typing import Iterable, Optional, Tuple
  import torch
  from linear_operator.operators import LinearOperator
  from torch import LongTensor, Tensor
  from ..distributions import Delta, MultivariateNormal
  from ..models import ApproximateGP
  from ..variational._variational_distribution import _VariationalDistribution
  from ..variational.grid_interpolation_variational_strategy import GridInterpolationVariationalStrategy
label_desc: Use image node_id 4 for calling the AdditiveGridInterpolationVariationalStrategy obj's underlying member method code with example usage: obj.forward(x, inducing_points, inducing_values, variational_inducing_covar) and returns: output, MultivariateNormal, Delta