class_docstr: NCP (Nearly-Constant Position) dynamic model. May be subclassed, e.g., with
  CWNV (Continuous White Noise Velocity) or DWNV (Discrete White Noise
  Velocity).
  
  :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.
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docstr: Compute and return PV state from native state. Useful for combining
  state estimates of different types in IMM (Interacting Multiple Model)
  filtering.
  
  :param x: native state estimate mean.
  :return: PV state estimate mean.
docstr_tok: ["Compute","and","return","PV","state","from","native","state",".","Useful","for","combining","state","estimates","of","different","types","in","IMM","(","Interacting","Multiple","Model",")","filtering",".",":","param","x",":","native","state","estimate","mean",".",":","return",":","PV","state","estimate","mean","."]
code: def mean2pv(self, x):
      """
      Compute and return PV state from native state. Useful for combining
      state estimates of different types in IMM (Interacting Multiple Model)
      filtering.
  
      :param x: native state estimate mean.
      :return: PV state estimate mean.
      """
      with torch.no_grad():
          x_pv = torch.zeros(
              2 * self._dimension, dtype=x.dtype, device=x.device
          )
          x_pv[: self._dimension] = x
      return x_pv
  
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 3 for calling the Ncp obj's underlying member method code with example usage: obj.mean2pv(x) and returns: x_pv