code: def prior_distribution(self) -> MultivariateNormal:
      # If desired, models can compare the input to forward to inducing_points and use a GridKernel for space
      # efficiency.
      # However, when using a default VariationalDistribution which has an O(m^2) space complexity anyways,
      # we find that GridKernel is typically not worth it due to the moderate slow down of using FFTs.
      out = super(
          AdditiveGridInterpolationVariationalStrategy, self
      ).prior_distribution
      mean = out.mean.repeat(self.num_dim, 1)
      covar = out.lazy_covariance_matrix.repeat(self.num_dim, 1, 1)
      return MultivariateNormal(mean, covar)
  
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 2 for calling the AdditiveGridInterpolationVariationalStrategy obj's underlying member method code with example usage: obj.prior_distribution() and returns: MultivariateNormal