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","."]
docstr: Measurement map (h) for predicting a measurement ``z`` from target
  state ``x``.
  
  :param x: PV state.
  :param do_normalization: whether to normalize output. Has no effect for
        this subclass.
  :return: Measurement predicted from state ``x``.
docstr_tok: ["Measurement","map","(","h",")","for","predicting","a","measurement","``","z","``","from","target","state","``","x","``",".",":","param","x",":","PV","state",".",":","param","do_normalization",":","whether","to","normalize","output",".","Has","no","effect","for","this","subclass",".",":","return",":","Measurement","predicted","from","state","``","x","``","."]
code: def __call__(self, x, do_normalization=True):
      """
      Measurement map (h) for predicting a measurement ``z`` from target
      state ``x``.
  
      :param x: PV state.
      :param do_normalization: whether to normalize output. Has no effect for
            this subclass.
      :return: Measurement predicted from state ``x``.
      """
      return x[: self._dimension]
  
imports: from abc import ABCMeta, abstractmethod
  import torch
  from pyro.distributions.util import eye_like
label_desc: Use image node_id 2 for calling the PositionMeasurement obj's underlying member method code with example usage: obj.__call__(x, do_normalization) and returns: x