code: def _forward(nat_mean, nat_covar):
      try:
          L_inv = psd_safe_cholesky(-2.0 * nat_covar, upper=False)
      except RuntimeError as e:
          if str(e).startswith("cholesky"):
              raise RuntimeError(
                  "Non-negative-definite natural covariance. You probably "
                  "updated it using an optimizer other than gpytorch.optim.NGD (such as Adam). "
                  "This is not supported."
              )
          else:
              raise e
      L = _triangular_inverse(L_inv, upper=False)
      S = L.transpose(-1, -2) @ L
      mu = (S @ nat_mean.unsqueeze(-1)).squeeze(-1)
      # Two choleskys are annoying, but we don't have good support for a
      # LinearOperator of form L.T @ L
      return mu, psd_safe_cholesky(S, upper=False)
  
imports: import abc
  import torch
  from linear_operator.operators import CholLinearOperator, TriangularLinearOperator
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from ..distributions import MultivariateNormal
  from ._variational_distribution import _VariationalDistribution
label_desc: Use image node_id 1 for calling the _NaturalToMuVarSqrt obj's underlying member method code with example usage: obj._forward(nat_mean, nat_covar) and returns: mu,  psd_safe_cholesky