class_docstr: NCV (Nearly-Constant Velocity) dynamic model. May be subclassed, e.g., with
  CWNA (Continuous White Noise Acceleration) or DWNA (Discrete White Noise
  Acceleration).
  
  :param dimension: native state dimension.
  :param sa2: variance of acceleration. Usually chosen so that the standard
        deviation is roughly half of the max acceleration one would ever
        expect to observe.
class_docstr_tok: ["NCV","(","Nearly-Constant","Velocity",")","dynamic","model",".","May","be","subclassed",",","e.g.",",","with","CWNA","(","Continuous","White","Noise","Acceleration",")","or","DWNA","(","Discrete","White","Noise","Acceleration",")",".",":","param","dimension",":","native","state","dimension",".",":","param","sa2",":","variance","of","acceleration",".","Usually","chosen","so","that","the","standard","deviation","is","roughly","half","of","the","max","acceleration","one","would","ever","expect","to","observe","."]
docstr: Compute and return cached native state transition Jacobian (F) over
  time interval ``dt``.
  
  :param dt: time interval to integrate over.
  :return: Read-only Jacobian (F) of integration map (f).
docstr_tok: ["Compute","and","return","cached","native","state","transition","Jacobian","(","F",")","over","time","interval","``","dt","``",".",":","param","dt",":","time","interval","to","integrate","over",".",":","return",":","Read-only","Jacobian","(","F",")","of","integration","map","(","f",")","."]
code: def jacobian(self, dt):
      """
      Compute and return cached native state transition Jacobian (F) over
      time interval ``dt``.
  
      :param dt: time interval to integrate over.
      :return: Read-only Jacobian (F) of integration map (f).
      """
      if dt not in self._F_cache:
          d = self._dimension
          with torch.no_grad():
              F = eye_like(self.sa2, d)
              F[: d // 2, d // 2 :] = dt * eye_like(self.sa2, d // 2)
          self._F_cache[dt] = F
  
      return self._F_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 5 for calling the Ncv obj's underlying member method code with example usage: obj.jacobian(dt) and returns: self