class_docstr: DynamicModel for which state transition Jacobians can be efficiently
  calculated, usu. analytically or by automatic differentiation.
class_docstr_tok: ["DynamicModel","for","which","state","transition","Jacobians","can","be","efficiently","calculated",",","usu",".","analytically","or","by","automatic","differentiation","."]
docstr: Compute and return 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","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 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).
      """
      raise NotImplementedError
  
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 DifferentiableDynamicModel obj's underlying member method code with example usage: obj.jacobian(dt) without return types