class_docstr: NCP (Nearly-Constant Position) dynamic model with CWNV (Continuous White
  Noise Velocity).
  
  References:
      "Estimation with Applications to Tracking and Navigation" by Y. Bar-
      Shalom et al, 2001, p.269.
  
  :param dimension: native state dimension.
  :param sv2: variance of velocity. Usually chosen so that the standard
        deviation is roughly half of the max velocity one would ever expect
        to observe.
class_docstr_tok: ["NCP","(","Nearly-Constant","Position",")","dynamic","model","with","CWNV","(","Continuous","White","Noise","Velocity",")",".","References",":","``","Estimation","with","Applications","to","Tracking","and","Navigation","''","by","Y.","Bar-","Shalom","et","al",",","2001",",","p.269",".",":","param","dimension",":","native","state","dimension",".",":","param","sv2",":","variance","of","velocity",".","Usually","chosen","so","that","the","standard","deviation","is","roughly","half","of","the","max","velocity","one","would","ever","expect","to","observe","."]
docstr: Compute and return cached process noise covariance (Q).
  
  :param dt: time interval to integrate over.
  :return: Read-only covariance (Q) of the native state ``x`` resulting from
      stochastic integration (for use with EKF).
docstr_tok: ["Compute","and","return","cached","process","noise","covariance","(","Q",")",".",":","param","dt",":","time","interval","to","integrate","over",".",":","return",":","Read-only","covariance","(","Q",")","of","the","native","state","``","x","``","resulting","from","stochastic","integration","(","for","use","with","EKF",")","."]
code: def process_noise_cov(self, dt=0.0):
      """
      Compute and return cached process noise covariance (Q).
  
      :param dt: time interval to integrate over.
      :return: Read-only covariance (Q) of the native state ``x`` resulting from
          stochastic integration (for use with EKF).
      """
      if dt not in self._Q_cache:
          # q: continuous-time process noise intensity with units
          #   length^2/time (m^2/s). Choose ``q`` so that changes in position,
          #   over a sampling period ``dt``, are roughly ``sqrt(q*dt)``.
          q = self.sv2 * dt
          Q = q * dt * eye_like(self.sv2, self._dimension)
          self._Q_cache[dt] = Q
  
      return self._Q_cache[dt]
  
imports: from abc import ABCMeta, abstractmethod
  import torch
  from torch import nn
  from torch.nn import Parameter
  import pyro.distributions
  from pyro.distributions.util import eye_like
label_desc: Use image node_id 1 for calling the NcpContinuous obj's underlying member method code with example usage: obj.process_noise_cov(dt) and returns: self