class_docstr: A :obj:`~gpytorch.variational._VariationalDistribution` that is defined to be a multivariate normal distribution
  with a full covariance matrix.
  
  The most common way this distribution is defined is to parameterize it in terms of a mean vector and a covariance
  matrix. In order to ensure that the covariance matrix remains positive definite, we only consider the lower
  triangle.
  
  :param num_inducing_points: Size of the variational distribution. This implies that the variational mean
      should be this size, and the variational covariance matrix should have this many rows and columns.
  :param batch_shape: Specifies an optional batch size
      for the variational parameters. This is useful for example when doing additive variational inference.
  :param mean_init_std: (Default: 1e-3) Standard deviation of gaussian noise to add to the mean initialization.
code: def initialize_variational_distribution(
      self, prior_dist: MultivariateNormal
  ) -> None:
      self.variational_mean.data.copy_(prior_dist.mean)
      self.variational_mean.data.add_(
          torch.randn_like(prior_dist.mean), alpha=self.mean_init_std
      )
      self.chol_variational_covar.data.copy_(
          prior_dist.lazy_covariance_matrix.cholesky().to_dense()
      )
  
file_path: gpytorch/gpytorch/variational/cholesky_variational_distribution.py
imports: import torch
  from linear_operator.operators import CholLinearOperator, TriangularLinearOperator
  from ..distributions import MultivariateNormal
  from ._variational_distribution import _VariationalDistribution
label_desc: Use image node_id 3 for calling the CholeskyVariationalDistribution obj's underlying member method code with example usage: obj.initialize_variational_distribution(prior_dist) without return types