class_docstr: Full-rank Gaussian position measurement in Euclidean space.
  
  :param mean: mean of measurement distribution.
  :param cov: covariance of measurement distribution.
  :param time: time of measurement.
class_docstr_tok: ["Full-rank","Gaussian","position","measurement","in","Euclidean","space",".",":","param","mean",":","mean","of","measurement","distribution",".",":","param","cov",":","covariance","of","measurement","distribution",".",":","param","time",":","time","of","measurement","."]
code: def __init__(self, mean, cov, time=None, frame_num=None):
      super().__init__(mean, cov, time=time, frame_num=frame_num)
      self._jacobian = torch.cat(
          [
              eye_like(mean, self.dimension),
              torch.zeros(
                  self.dimension,
                  self.dimension,
                  dtype=mean.dtype,
                  device=mean.device,
              ),
          ],
          dim=1,
      )
  
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 PositionMeasurement object from inherited base classes: DifferentiableMeasurement with example: obj = PositionMeasurement(mean, cov, time, frame_num)