code: def _compute_grid(self, inputs: Tensor) -> Tuple[LongTensor, Tensor]:
      num_data, num_dim = inputs.size()
      inputs = inputs.transpose(0, 1).reshape(-1, 1)
      interp_indices, interp_values = super(
          AdditiveGridInterpolationVariationalStrategy, self
      )._compute_grid(inputs)
      interp_indices = interp_indices.view(num_dim, num_data, -1)
      interp_values = interp_values.view(num_dim, num_data, -1)
  
      if hasattr(self, "mixing_params"):
          interp_values = interp_values.mul(
              self.mixing_params.unsqueeze(1).unsqueeze(2)
          )
      return interp_indices, interp_values
  
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 3 for calling the AdditiveGridInterpolationVariationalStrategy obj's underlying member method code with example usage: obj._compute_grid(inputs) and returns: interp_indices,  interp_values