class_docstr: Multivariate Normal prior
  
  pdf(x) = det(2 * pi * Sigma)^-0.5 * exp(-0.5 * (x - mu)' Sigma^-1 (x - mu))
  
  where mu is the mean and Sigma > 0 is the covariance matrix.
class_docstr_tok: ["Multivariate","Normal","prior","pdf","(","x",")","=","det","(","2","*","pi","*","Sigma",")","^-0.5","*","exp","(","-0.5","*","(","x","-","mu",")","'","Sigma^-1","(","x","-","mu",")",")","where","mu","is","the","mean","and","Sigma",">","0","is","the","covariance","matrix","."]
docstr: Applies module-level cpu() call and resets all lazy properties
code: def cpu(self):
      """Applies module-level cpu() call and resets all lazy properties"""
      module = self._apply(lambda t: t.cpu())
      _del_attributes(module, MVN_LAZY_PROPERTIES)
      return module
  
imports: import torch
  from torch.distributions import Gamma, HalfCauchy, HalfNormal, LogNormal, MultivariateNormal, Normal, Uniform
  from torch.nn import Module
  from .prior import Prior
  from .utils import _bufferize_attributes, _del_attributes
label_desc: Use image node_id 3 for calling the MultivariateNormalPrior obj's underlying member method code with example usage: obj.cpu() and returns: module