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.
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docstr: Integrate native state ``x`` over time interval ``dt``.
  
  :param x: current native state. If the DynamicModel is non-differentiable,
        be sure to handle the case of ``x`` being augmented with process
        noise parameters.
  :param dt: time interval to integrate over.
  :param do_normalization: whether to perform normalization on output, e.g.,
        mod'ing angles into an interval. Has no effect for this subclass.
  
  :return: Native state x integrated dt into the future.
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code: def forward(self, x, dt, do_normalization=True):
      """
      Integrate native state ``x`` over time interval ``dt``.
  
      :param x: current native state. If the DynamicModel is non-differentiable,
            be sure to handle the case of ``x`` being augmented with process
            noise parameters.
      :param dt: time interval to integrate over.
      :param do_normalization: whether to perform normalization on output, e.g.,
            mod'ing angles into an interval. Has no effect for this subclass.
  
      :return: Native state x integrated dt into the future.
      """
      F = self.jacobian(dt)
      return F.mm(x.unsqueeze(1)).squeeze(1)
  
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 2 for calling the Ncv obj's underlying member method code with example usage: obj.forward(x, dt, do_normalization) and returns: F