class_docstr: This :obj:`~gpytorch.variational._VariationalDistribution` object replaces a variational distribution
  with a single particle. It is equivalent to doing MAP inference.
  
  :param int 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.
  :type batch_shape: :obj:`torch.Size`, optional
  :param float mean_init_std: (Default: 1e-3) Standard deviation of gaussian noise to add to the mean initialization.
code: def forward(self) -> Distribution:
      return Delta(self.variational_mean)
  
imports: import torch
  from ..distributions import Delta, Distribution, MultivariateNormal
  from ._variational_distribution import _VariationalDistribution
label_desc: Use image node_id 2 for calling the DeltaVariationalDistribution obj's underlying member method code with example usage: obj.forward() and returns: Delta