class_docstr: Multivariate normal (Gaussian) distribution with OMT gradients w.r.t. both
  parameters. Note the gradient computation w.r.t. the Cholesky factor has cost
  O(D^3), although the resulting gradient variance is generally expected to be lower.
  
  A distribution over vectors in which all the elements have a joint Gaussian
  density.
  
  :param torch.Tensor loc: Mean.
  :param torch.Tensor scale_tril: Cholesky of Covariance matrix.
code: def __init__(self, loc, scale_tril):
      if loc.dim() != 1:
          raise ValueError(
              "OMTMultivariateNormal loc must be 1-dimensional"
          )
      if scale_tril.dim() != 2:
          raise ValueError(
              "OMTMultivariateNormal scale_tril must be 2-dimensional"
          )
      super().__init__(loc, scale_tril=scale_tril)
  
file_path: pyro/pyro/distributions/omt_mvn.py
imports: import torch
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.distributions import constraints
  from pyro.distributions.torch import MultivariateNormal
  from pyro.distributions.util import eye_like, sum_leftmost
label_desc: Use image node_id 1 to create a new OMTMultivariateNormal object from inherited base classes: MultivariateNormal with example: obj = OMTMultivariateNormal(loc, scale_tril)