class_docstr: Gaussian measurement interface.
  
  :param mean: mean of measurement distribution.
  :param cov: covariance of measurement distribution.
  :param time: continuous time of measurement. If this is not
        provided, `frame_num` must be.
  :param frame_num: discrete time of measurement. If this is not
        provided, `time` must be.
class_docstr_tok: ["Gaussian","measurement","interface",".",":","param","mean",":","mean","of","measurement","distribution",".",":","param","cov",":","covariance","of","measurement","distribution",".",":","param","time",":","continuous","time","of","measurement",".","If","this","is","not","provided",",","`","frame_num","`","must","be",".",":","param","frame_num",":","discrete","time","of","measurement",".","If","this","is","not","provided",",","`","time","`","must","be","."]
code: def __init__(self, mean, cov, time=None, frame_num=None):
      self._dimension = len(mean)
      self._mean = mean
      self._cov = cov
      if time is None and frame_num is None:
          raise ValueError("Must provide time or frame_num!")
      self._time = time
      self._frame_num = frame_num
  
imports: from abc import ABCMeta, abstractmethod
  import torch
  from pyro.distributions.util import eye_like
label_desc: Use image node_id 1 to create a new Measurement object from inherited base classes: object with example: obj = Measurement(mean, cov, time, frame_num)