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 __init__(
      self,
      loc,
      cov=None,
      precision=None,
      scale_tril=None,
      validate_args=None,
  ):
      if (cov is not None) + (precision is not None) + (
          scale_tril is not None
      ) != 1:
          raise ValueError(
              "Exactly one onf `cov` or `precision` or "
              + "`scale_tril` may be specified"
          )
      self.loc = loc
      if cov is not None:
          self.cov = cov
      elif precision is not None:
          self.precision = precision
      else:
          self.scale_tril = scale_tril
      super(MultivariateNormal, self).__init__(
          event_dim=1, validate_args=validate_args
      )
  
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 1 to create a new MultivariateNormal object from inherited base classes: Distribution with example: obj = MultivariateNormal(loc, cov, precision, scale_tril, validate_args)