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 rsample(self, sample_shape=torch.Size()):
      return _OMTMVNSample.apply(
          self.loc, self.scale_tril, sample_shape + self.loc.shape
      )
  
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 2 for calling the OMTMultivariateNormal obj's underlying member method code with example usage: obj.rsample(sample_shape) and returns: _OMTMVNSample