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.
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code: def __init__(
      self,
      loc,
      covariance_matrix=None,
      precision_matrix=None,
      scale_tril=None,
      validate_args=False,
      transform=None,
  ):
      TModule.__init__(self)
      MultivariateNormal.__init__(
          self,
          loc=loc,
          covariance_matrix=covariance_matrix,
          precision_matrix=precision_matrix,
          scale_tril=scale_tril,
          validate_args=validate_args,
      )
      _bufferize_attributes(self, ("loc", "_unbroadcasted_scale_tril"))
      self._transform = transform
  
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 1 to create a new MultivariateNormalPrior object from inherited base classes: Prior, MultivariateNormal with example: obj = MultivariateNormalPrior(loc, covariance_matrix, precision_matrix, scale_tril, validate_args, transform)