parameterized by a mean vector and a covariance matrix.
The multivariate normal distribution can be parameterized either in
   terms of a positive definite covariance matrix \mathbf{\Sigma} or a
   positive definite precision matrix \mathbf{\Sigma}^{-1} or a lower-
   triangular matrix \mathbf{L} with positive-valued diagonal entries,
   such that \mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top. This
   triangular matrix can be obtained via e.g. Cholesky decomposition
   of the covariance.
-[ Example ]-



m = MultivariateNormal(torch.zeros(2), torch.eye(2))
m.sample()  # normally distributed with mean=[0,0] and covariance_matrix=I
   tensor([-0.2102, -0.5429])



Parameters:
      * loc (Tensor) -- mean of the distribution
  * **covariance_matrix** (*Tensor*) -- positive-definite
    covariance matrix

  * **precision_matrix** (*Tensor*) -- positive-definite precision
    matrix

  * **scale_tril** (*Tensor*) -- lower-triangular factor of
