class_docstr: This class is used by variational GPLVM models.
  It adds the KL divergence between two multivariate Gaussian distributions:
  scaled by the size of the data and the number of output dimensions.
  
  .. math::
  
      D_\text{KL} \left( q(\mathbf x) \Vert p(\mathbf x) \right)
  
  
  :param q_x: The MVN distribution :math:`q(\mathbf x)`.
  :param p_x: The MVN distribution :math:`p(\mathbf x)`.
  :param n: Size of the latent space.
  :param data_dim: Dimensionality of the :math:`\mathbf Y` values.
code: def __init__(
      self,
      q_x: MultivariateNormal,
      p_x: MultivariateNormal,
      n: int,
      data_dim: int,
  ):
      super().__init__()
      self.q_x = q_x
      self.p_x = p_x
      self.n = n
      self.data_dim = data_dim
  
imports: from torch.distributions import kl_divergence
  from ..distributions import MultivariateNormal
  from .added_loss_term import AddedLossTerm
label_desc: Use image node_id 1 to create a new KLGaussianAddedLossTerm object from inherited base classes: AddedLossTerm with example: obj = KLGaussianAddedLossTerm(q_x, p_x, n, data_dim)