class_docstr: Create a multivaraite Normal distribution object.
  
  Parameters
  ----------
  loc : Tensor
      mean of the distribution.
  cov : Tensor
      covariance matrix of the distribution
  precision : Tensor
      precision matrix of the distribution
  scale_tril : Tensor
      lower-triangular factor of the covariance
class_docstr_tok: ["Create","a","multivaraite","Normal","distribution","object",".","Parameters","--","--","--","--","--","loc",":","Tensor","mean","of","the","distribution",".","cov",":","Tensor","covariance","matrix","of","the","distribution","precision",":","Tensor","precision","matrix","of","the","distribution","scale_tril",":","Tensor","lower-triangular","factor","of","the","covariance"]
code: def precision(self):
      # pylint: disable=method-hidden
      if "cov" in self.__dict__:
          return np.linalg.inv(self.cov)
      scale_tril_inv = np.linalg.inv(self.scale_tril)
      return np.matmul(scale_tril_inv.mT, scale_tril_inv)
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/multivariate_normal.py
imports: import math
  from .distribution import Distribution
  from .constraint import Real, PositiveDefinite, LowerCholesky
  from .utils import cached_property
  from ....None import np
label_desc: Use image node_id 5 for calling the MultivariateNormal obj's underlying member method code with example usage: obj.precision() and returns: np, np