class_docstr: Abstract base class for all Variational Distributions.
  
  :ivar torch.dtype dtype: The dtype of the VariationalDistribution parameters
  :ivar torch.dtype device: The device of the VariationalDistribution parameters
class_docstr_tok: ["Abstract","base","class","for","all","Variational","Distributions",".",":","ivar","torch.dtype","dtype",":","The","dtype","of","the","VariationalDistribution","parameters",":","ivar","torch.dtype","device",":","The","device","of","the","VariationalDistribution","parameters"]
docstr: Constructs and returns the variational distribution
  
  :rtype: ~gpytorch.distributions.MultivariateNormal
  :return: The distribution :math:`q(\mathbf u)`
docstr_tok: ["Constructs","and","returns","the","variational","distribution",":","rtype",":","~gpytorch.distributions.MultivariateNormal",":","return",":","The","distribution",":","math",":","`","q","(","\\mathbf","u",")","`"]
code: def forward(self) -> Distribution:
      r"""
      Constructs and returns the variational distribution
  
      :rtype: ~gpytorch.distributions.MultivariateNormal
      :return: The distribution :math:`q(\mathbf u)`
      """
      raise NotImplementedError
  
file_path: gpytorch/gpytorch/variational/_variational_distribution.py
imports: from abc import ABC, abstractmethod
  import torch
  from ..distributions import Distribution, MultivariateNormal
  from ..module import Module
label_desc: Use image node_id 4 for calling the _VariationalDistribution obj's underlying member method code with example usage: obj.forward() without return types