code: def _forward(
      nat_mean: Tensor, tril_nat_covar: Tensor
  ) -> Tuple[Tensor, Tensor]:
      L = _triangular_inverse(tril_nat_covar, upper=False)
      mu = L @ (L.transpose(-1, -2) @ nat_mean.unsqueeze(-1))
      return mu.squeeze(-1), L
  
file_path: gpytorch/gpytorch/variational/tril_natural_variational_distribution.py
imports: from typing import Tuple
  import torch
  from linear_operator.operators import CholLinearOperator, TriangularLinearOperator
  from torch import Tensor
  from torch.autograd.function import FunctionCtx
  from ..distributions import Distribution, MultivariateNormal
  from .natural_variational_distribution import _NaturalToMuVarSqrt, _NaturalVariationalDistribution, _phi_for_cholesky_, _triangular_inverse
label_desc: Use image node_id 1 for calling the _TrilNaturalToMuVarSqrt obj's underlying member method code with example usage: obj._forward(nat_mean, tril_nat_covar) and returns: mu,  L