def METHOD_NAME(self, x, inducing_points, inducing_values, variational_inducing_covar=None):
    if variational_inducing_covar is None:
        raise RuntimeError(
            "GridInterpolationVariationalStrategy is only compatible with Gaussian variational "
            f"distributions. Got ({self.variational_distribution.__class__.__name__}."
        )
    variational_distribution = self.variational_distribution
    # Get interpolations
    interp_indices, interp_values = self._compute_grid(x)
    # Compute test mean
    # Left multiply samples by interpolation matrix
    predictive_mean = left_interp(interp_indices, interp_values, inducing_values.unsqueeze(-1))
    predictive_mean = predictive_mean.squeeze(-1)
    # Compute test covar
    predictive_covar = InterpolatedLinearOperator(
        variational_distribution.lazy_covariance_matrix,
        interp_indices,
        interp_values,
        interp_indices,
        interp_values,
    )
    output = MultivariateNormal(predictive_mean, predictive_covar)
    return output