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: Method for initializing the variational distribution, based on the prior distribution.
  
  :param ~gpytorch.distributions.Distribution prior_dist: The prior distribution :math:`p(\mathbf u)`.
docstr_tok: ["Method","for","initializing","the","variational","distribution",",","based","on","the","prior","distribution",".",":","param","~gpytorch.distributions.Distribution","prior_dist",":","The","prior","distribution",":","math",":","`","p","(","\\mathbf","u",")","`","."]
code: def initialize_variational_distribution(
      self, prior_dist: MultivariateNormal
  ) -> None:
      r"""
      Method for initializing the variational distribution, based on the prior distribution.
  
      :param ~gpytorch.distributions.Distribution prior_dist: The prior distribution :math:`p(\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 6 for calling the _VariationalDistribution obj's underlying member method code with example usage: obj.initialize_variational_distribution(prior_dist) without return types