class_docstr: Constructs a multivariate normal random variable, based on mean and covariance.
  Can be multivariate, or a batch of multivariate normals
  
  Passing a vector mean corresponds to a multivariate normal.
  Passing a matrix mean corresponds to a batch of multivariate normals.
  
  :param mean: `... x N` mean of mvn distribution.
  :param covariance_matrix: `... x N X N` covariance matrix of mvn distribution.
  :param validate_args: If True, validate `mean` anad `covariance_matrix` arguments. (Default: False.)
  
  :ivar torch.Size base_sample_shape: The shape of a base sample (without
      batching) that is used to generate a single sample.
  :ivar torch.Tensor covariance_matrix: The covariance matrix, represented as a dense :class:`torch.Tensor`
  :ivar ~linear_operator.LinearOperator lazy_covariance_matrix: The covariance matrix, represented
      as a :class:`~linear_operator.LinearOperator`.
  :ivar torch.Tensor mean: The mean.
  :ivar torch.Tensor stddev: The standard deviation.
  :ivar torch.Tensor variance: The variance.
docstr: Returns 2 standard deviations above and below the mean.
  
  :return: Pair of tensors of size `... x N`, where N is the
      dimensionality of the random variable. The first (second) Tensor is the
      lower (upper) end of the confidence region.
docstr_tok: ["Returns","2","standard","deviations","above","and","below","the","mean",".",":","return",":","Pair","of","tensors","of","size","`","...","x","N","`",",","where","N","is","the","dimensionality","of","the","random","variable",".","The","first","(","second",")","Tensor","is","the","lower","(","upper",")","end","of","the","confidence","region","."]
code: def confidence_region(self) -> Tuple[Tensor, Tensor]:
      """
      Returns 2 standard deviations above and below the mean.
  
      :return: Pair of tensors of size `... x N`, where N is the
          dimensionality of the random variable. The first (second) Tensor is the
          lower (upper) end of the confidence region.
      """
      std2 = self.stddev.mul_(2)
      mean = self.mean
      return mean.sub(std2), mean.add(std2)
  
imports: from __future__ import annotations
  import math
  import warnings
  from numbers import Number
  from typing import Optional, Tuple, Union
  import torch
  from linear_operator import to_dense, to_linear_operator
  from linear_operator.operators import DiagLinearOperator, LinearOperator, RootLinearOperator
  from torch import Tensor
  from torch.distributions import MultivariateNormal
  from torch.distributions.kl import register_kl
  from torch.distributions.utils import _standard_normal, lazy_property
  from ..None import settings
  from ..utils.warnings import NumericalWarning
  from .distribution import Distribution
label_desc: Use image node_id 9 for calling the MultivariateNormal obj's underlying member method code with example usage: obj.confidence_region() and returns: mean,  mean